{"id":832,"date":"2017-08-29T17:55:42","date_gmt":"2017-08-29T17:55:42","guid":{"rendered":"https:\/\/fei-lab.org\/?p=832"},"modified":"2023-12-28T20:01:18","modified_gmt":"2023-12-28T20:01:18","slug":"quantitative-imaging-analysis","status":"publish","type":"post","link":"https:\/\/fei-lab.org\/quantitative-imaging-analysis\/","title":{"rendered":"Quantitative Imaging Analysis"},"content":{"rendered":"\n<style type=\"text\/css\" data-created_by=\"avia_inline_auto\" id=\"style-css-av-av_heading-b69e88da76102c6ef8275fa5dd14ae14\">\n#top .av-special-heading.av-av_heading-b69e88da76102c6ef8275fa5dd14ae14{\npadding-bottom:10px;\n}\nbody .av-special-heading.av-av_heading-b69e88da76102c6ef8275fa5dd14ae14 .av-special-heading-tag .heading-char{\nfont-size:25px;\n}\n.av-special-heading.av-av_heading-b69e88da76102c6ef8275fa5dd14ae14 .av-subheading{\nfont-size:15px;\n}\n<\/style>\n<div  class='av-special-heading av-av_heading-b69e88da76102c6ef8275fa5dd14ae14 av-special-heading-h3  avia-builder-el-0  el_before_av_one_full  avia-builder-el-first '><h3 class='av-special-heading-tag '  itemprop=\"headline\"  >Quantitative Imaging Analysis<\/h3><div class=\"special-heading-border\"><div class=\"special-heading-inner-border\"><\/div><\/div><\/div>\n\n<style type=\"text\/css\" data-created_by=\"avia_inline_auto\" id=\"style-css-av-20cnm8-c2bb425b724a40fd867668b2e7a00019\">\n.flex_column.av-20cnm8-c2bb425b724a40fd867668b2e7a00019{\nborder-radius:0px 0px 0px 0px;\npadding:0px 0px 0px 0px;\n}\n<\/style>\n<div  class='flex_column av-20cnm8-c2bb425b724a40fd867668b2e7a00019 av_one_full  avia-builder-el-1  el_after_av_heading  el_before_av_hr  first flex_column_div av-zero-column-padding  '     ><section  class='av_textblock_section av-8ajk0-4686f310db65c193bf8cd5c95380994d '   itemscope=\"itemscope\" itemtype=\"https:\/\/schema.org\/BlogPosting\" itemprop=\"blogPost\" ><div class='avia_textblock'  itemprop=\"text\" ><p align=\"justify\">In medical applications, multiple images are acquired from the same subject at different times or from different subjects. The critical stage for utilizing these images is to align them in order to visualize the combined information. Image registration includes the processing to resolve the mapping between images so that the features or structures in one image correspond to those in the other image. The transformation between two image scenes can be of either rigid or non-rigid. Rigid body transformation has six degrees of freedom in three dimensions, i.e. three translations and three rotations. For most body organs, the motion is non-rigid and requires more degrees of freedom to accurately describe tissue motion. Therefore, deformable registration becomes necessary for many image applications. One example is the registration of pre- and post-contrast enhanced breast MRI images. Deformable registration is required for this application as soft tissue, such as breast tissue, always undergoes non-rigid motion between images. Other similar applications can be found during medical imaging diagnosis where different modality images or the same modality at different times, are acquired and require deformable registration because of the non-rigid tissue deformation between images. Examples include heart imaging and chest PET-CT imaging where non-rigid motion can be a major tissue motion source. In image-guided radiation therapy, because of the type of treatment or patient respiration, a non-rigid shape or position change in an organ is unavoidable. Deformable image registration is critical for delivering an appropriate radiation dose and in order to avoid the damage to adjacent healthy tissue. An example is image-guided radiation therapy of prostate tumors or tumors in other organs.<\/p>\n<\/div><\/section><\/div>\n\n<style type=\"text\/css\" data-created_by=\"avia_inline_auto\" id=\"style-css-av-1597k-8d788005b04e3da45a2f36dc2f6996e0\">\n#top .hr.hr-invisible.av-1597k-8d788005b04e3da45a2f36dc2f6996e0{\nheight:10px;\n}\n<\/style>\n<div  class='hr av-1597k-8d788005b04e3da45a2f36dc2f6996e0 hr-invisible  avia-builder-el-3  el_after_av_one_full  el_before_av_toggle_container '><span class='hr-inner '><span class=\"hr-inner-style\"><\/span><\/span><\/div>\n\n<style type=\"text\/css\" data-created_by=\"avia_inline_auto\" id=\"style-css-av-k5xzkiyx-5d0bed83ad6298b2c5f4acd5fe58a9f7\">\n#top .togglecontainer.av-k5xzkiyx-5d0bed83ad6298b2c5f4acd5fe58a9f7 p.toggler{\ncolor:#444444;\nborder-color:#444444;\n}\n#top .togglecontainer.av-k5xzkiyx-5d0bed83ad6298b2c5f4acd5fe58a9f7 p.toggler .toggle_icon{\ncolor:#444444;\nborder-color:#444444;\n}\n#top .togglecontainer.av-k5xzkiyx-5d0bed83ad6298b2c5f4acd5fe58a9f7 .toggle_wrap .toggle_content{\ncolor:#444444;\nborder-color:#444444;\n}\n<\/style>\n<div  class='togglecontainer av-k5xzkiyx-5d0bed83ad6298b2c5f4acd5fe58a9f7 av-minimal-toggle  avia-builder-el-4  el_after_av_hr  el_before_av_hr  toggle_close_all' >\n<section class='av_toggle_section av-4hpmo-10a13c2cbce78ebf1638ae955bb7636f'  itemscope=\"itemscope\" itemtype=\"https:\/\/schema.org\/BlogPosting\" itemprop=\"blogPost\" ><div role=\"tablist\" class=\"single_toggle\" data-tags=\"{All} \"  ><p id='toggle-toggle-id-1' data-fake-id='#toggle-id-1' class='toggler  av-title-above av-inherit-font-color hasCustomColor av-inherit-border-color'  itemprop=\"headline\"  role='tab' tabindex='0' aria-controls='toggle-id-1' data-slide-speed=\"200\" data-title=\"Thin-Plate Spline (TPS) Deformable Image Registration\" data-title-open=\"\" data-aria_collapsed=\"Click to expand: Thin-Plate Spline (TPS) Deformable Image Registration\" data-aria_expanded=\"Click to collapse: Thin-Plate Spline (TPS) Deformable Image Registration\">Thin-Plate Spline (TPS) Deformable Image Registration<span class=\"toggle_icon\"><span class=\"vert_icon\"><\/span><span class=\"hor_icon\"><\/span><\/span><\/p><div id='toggle-id-1' aria-labelledby='toggle-toggle-id-1' role='region' class='toggle_wrap  av-title-above'  ><div class='toggle_content invers-color av-inherit-font-color hasCustomColor av-inherit-border-color'  itemprop=\"text\" ><p>We created and evaluated an almost fully automated, 3D non-rigid registration algorithm using mutual information and a thin-plate spline (TPS) transformation for MR images of the prostate and pelvis. In the first step, an automatic rigid-body registration with special features was used to capture the global transformation. In the second step, local feature points were registered. An operator entered only five feature points (FPs) located at the prostate center, in the left and right hip joints, and in the left and right distal femurs. The program automatically determined and optimized other FPs on the external pelvic skin surface and along the femurs. More than 600 control points were used to establish a TPS transformation for deformation of the pelvic region and the prostate. Ten volume pairs were acquired from three volunteers in the diagnostic (supine) and treatment positions (supine with legs raised). Various visualization techniques showed that warping rectified the significant pelvic misalignment caused by the rigid body method. Gray-value measures of registration quality, including mutual information, correlation coefficient, and intensity difference, all improved with warping. The distance between prostate 3D centroids was 0.7 \u00b1 0.2 mm following warping compared to 4.9 \u00b1 3.4 mm with rigid-body registration. The semiautomatic non-rigid registration works better than rigid body registration when the patient position is changes significantly between acquisitions; it could be a useful tool for many applications of prostate diagnosis and therapy.<\/p>\n<div id=\"attachment_153\" style=\"width: 1080px\" class=\"wp-caption aligncenter\"><img loading=\"lazy\" decoding=\"async\" aria-describedby=\"caption-attachment-153\" class=\"size-full wp-image-153\" src=\"https:\/\/fei-lab.org\/wp-content\/uploads\/2017\/03\/CI_Prostate_Seg.png\" alt=\"\" width=\"1070\" height=\"717\" srcset=\"https:\/\/fei-lab.org\/wp-content\/uploads\/2017\/03\/CI_Prostate_Seg.png 1070w, https:\/\/fei-lab.org\/wp-content\/uploads\/2017\/03\/CI_Prostate_Seg-300x201.png 300w, https:\/\/fei-lab.org\/wp-content\/uploads\/2017\/03\/CI_Prostate_Seg-768x515.png 768w, https:\/\/fei-lab.org\/wp-content\/uploads\/2017\/03\/CI_Prostate_Seg-1030x690.png 1030w, https:\/\/fei-lab.org\/wp-content\/uploads\/2017\/03\/CI_Prostate_Seg-705x472.png 705w, https:\/\/fei-lab.org\/wp-content\/uploads\/2017\/03\/CI_Prostate_Seg-450x302.png 450w\" sizes=\"auto, (max-width: 1070px) 100vw, 1070px\" \/><p id=\"caption-attachment-153\" class=\"wp-caption-text\">Our semi-automatic segmentation method for prostate MR images. The blue curves are the ground truth labeled by a radiologist, while the red curves are the segmentations of the proposed method (Tian Z, Liu L, Zhang Z, Fei B. Superpixel-Based Segmentation for 3D Prostate MR Images. IEEE Trans Med Imaging. 2016 Mar;35(3):791-801).<\/p><\/div>\n<\/div><\/div><\/div><\/section>\n<section class='av_toggle_section av-14gklk-0cadd32abb0d516956490f5dac2ec3a9'  itemscope=\"itemscope\" itemtype=\"https:\/\/schema.org\/BlogPosting\" itemprop=\"blogPost\" ><div role=\"tablist\" class=\"single_toggle\" data-tags=\"{All} \"  ><p id='toggle-toggle-id-2' data-fake-id='#toggle-id-2' class='toggler  av-title-above av-inherit-font-color hasCustomColor av-inherit-border-color'  itemprop=\"headline\"  role='tab' tabindex='0' aria-controls='toggle-id-2' data-slide-speed=\"200\" data-title=\"B-Spline Deformable Image Registration\" data-title-open=\"\" data-aria_collapsed=\"Click to expand: B-Spline Deformable Image Registration\" data-aria_expanded=\"Click to collapse: B-Spline Deformable Image Registration\">B-Spline Deformable Image Registration<span class=\"toggle_icon\"><span class=\"vert_icon\"><\/span><span class=\"hor_icon\"><\/span><\/span><\/p><div id='toggle-id-2' aria-labelledby='toggle-toggle-id-2' role='region' class='toggle_wrap  av-title-above'  ><div class='toggle_content invers-color av-inherit-font-color hasCustomColor av-inherit-border-color'  itemprop=\"text\" ><p>We implemented mutual information B-spline deformation registration algorithms. Mutual information does not assume a linear intensity relationship between images and has been used for registration of images of either the same modality or different modalities. A motion constraint is optimized in order to achieve a smooth function instead of an unrealistic result. A gradient-based minimization method is used to find the B-splines control coefficients for optimal transformation. Multiresolution strategy is applied to register the image from the downsampled low-resolution image to the original high-resolution images. The number of control points also hierarchally increase along the multiresolution framework. The deformation computed at low resolution is the initial transformation for the optimization at the high resolution.<\/p>\n<div id=\"attachment_86\" style=\"width: 378px\" class=\"wp-caption aligncenter\"><img loading=\"lazy\" decoding=\"async\" aria-describedby=\"caption-attachment-86\" class=\"wp-image-86 size-full\" src=\"https:\/\/fei-lab.org\/wp-content\/uploads\/2017\/03\/BSpline_Result_Fig.jpg\" alt=\"\" width=\"368\" height=\"128\" srcset=\"https:\/\/fei-lab.org\/wp-content\/uploads\/2017\/03\/BSpline_Result_Fig.jpg 368w, https:\/\/fei-lab.org\/wp-content\/uploads\/2017\/03\/BSpline_Result_Fig-300x104.jpg 300w\" sizes=\"auto, (max-width: 368px) 100vw, 368px\" \/><p id=\"caption-attachment-86\" class=\"wp-caption-text\">Figure 2. Deformable registration of 3D brain MR data. With deformable transformation, Image (a) is transformed into Image (b) with shape deformation in order to match the reference image (c).<\/p><\/div>\n<\/div><\/div><\/div><\/section>\n<section class='av_toggle_section av-379k8-97b588761df503dcd10ca935e5384933'  itemscope=\"itemscope\" itemtype=\"https:\/\/schema.org\/BlogPosting\" itemprop=\"blogPost\" ><div role=\"tablist\" class=\"single_toggle\" data-tags=\"{All} \"  ><p id='toggle-toggle-id-3' data-fake-id='#toggle-id-3' class='toggler  av-title-above av-inherit-font-color hasCustomColor av-inherit-border-color'  itemprop=\"headline\"  role='tab' tabindex='0' aria-controls='toggle-id-3' data-slide-speed=\"200\" data-title=\"Finite Element Model Based Deformable Registration\" data-title-open=\"\" data-aria_collapsed=\"Click to expand: Finite Element Model Based Deformable Registration\" data-aria_expanded=\"Click to collapse: Finite Element Model Based Deformable Registration\">Finite Element Model Based Deformable Registration<span class=\"toggle_icon\"><span class=\"vert_icon\"><\/span><span class=\"hor_icon\"><\/span><\/span><\/p><div id='toggle-id-3' aria-labelledby='toggle-toggle-id-3' role='region' class='toggle_wrap  av-title-above'  ><div class='toggle_content invers-color av-inherit-font-color hasCustomColor av-inherit-border-color'  itemprop=\"text\" ><p>For soft tissue, e.g. tumor, registration, we developed a finite element model (FEM)-based deformable registration method. We have applied this FEM registration method to tumor MRI and PET images. In the first step, we applied a rigid registration algorithm to align the cropped MRI and PET images using three translations and three rotations. After registration, we manually segmented the tumor slice-by-slice on both high-resolution MRI and PET image volumes. We then applied the deformable registration algorithm. The registration approach deforms the tumor surface from the MRI volume toward that from the PET image. The displacements at the surface vertices are the force that drives the elastic surface from MRI toward that from the PET image. The tumor was modeled as a linear isotopic elastic material. The FEM model was used to infer volumetric deformation of the tumor from the surface. The force is then integrated over each element and is distributed over the nodes belonging to the element using its shape functions. After obtaining the displacement field for all vertices, we used a linear interpolation to obtain the deformed image volume of the tumor.<\/p>\n<div id=\"attachment_87\" style=\"width: 564px\" class=\"wp-caption aligncenter\"><img loading=\"lazy\" decoding=\"async\" aria-describedby=\"caption-attachment-87\" class=\"size-full wp-image-87\" src=\"https:\/\/fei-lab.org\/wp-content\/uploads\/2017\/03\/FEM_Result_Fig.jpg\" alt=\"\" width=\"554\" height=\"219\" srcset=\"https:\/\/fei-lab.org\/wp-content\/uploads\/2017\/03\/FEM_Result_Fig.jpg 554w, https:\/\/fei-lab.org\/wp-content\/uploads\/2017\/03\/FEM_Result_Fig-300x119.jpg 300w, https:\/\/fei-lab.org\/wp-content\/uploads\/2017\/03\/FEM_Result_Fig-450x178.jpg 450w\" sizes=\"auto, (max-width: 554px) 100vw, 554px\" \/><p id=\"caption-attachment-87\" class=\"wp-caption-text\">Figure 3. Three-dimensional meshes of a tumor. (a) Tumor segmented from a high-resolution MR volume. (b) Same tumor from the corresponding microPET emission images. (c) Color overly of the tumor from MRI (yellow) and PET (red). The tumor deformed during the two imaging sessions.<\/p><\/div>\n<\/div><\/div><\/div><\/section>\n<section class='av_toggle_section av-sleig-fbfc9a795e7ee114f2e230fe2f4e02b8'  itemscope=\"itemscope\" itemtype=\"https:\/\/schema.org\/BlogPosting\" itemprop=\"blogPost\" ><div role=\"tablist\" class=\"single_toggle\" data-tags=\"{All} \"  ><p id='toggle-toggle-id-4' data-fake-id='#toggle-id-4' class='toggler  av-title-above av-inherit-font-color hasCustomColor av-inherit-border-color'  itemprop=\"headline\"  role='tab' tabindex='0' aria-controls='toggle-id-4' data-slide-speed=\"200\" data-title=\"Two-Dimensional (2D) Image Registration Software\" data-title-open=\"\" data-aria_collapsed=\"Click to expand: Two-Dimensional (2D) Image Registration Software\" data-aria_expanded=\"Click to collapse: Two-Dimensional (2D) Image Registration Software\">Two-Dimensional (2D) Image Registration Software<span class=\"toggle_icon\"><span class=\"vert_icon\"><\/span><span class=\"hor_icon\"><\/span><\/span><\/p><div id='toggle-id-4' aria-labelledby='toggle-toggle-id-4' role='region' class='toggle_wrap  av-title-above'  ><div class='toggle_content invers-color av-inherit-font-color hasCustomColor av-inherit-border-color'  itemprop=\"text\" ><p>The 2D registration program can align 2D images using rigid transformation. It integrates manual and automatic registration. It has the following features: (1) Register two 2D images automatically by intensity-based methods, (2) Register two 2D images manually. (3) Load and register floating multiple slices to the reference slice and display the registration parameters for each floating slice.<\/p>\n<div id=\"attachment_88\" style=\"width: 507px\" class=\"wp-caption aligncenter\"><img loading=\"lazy\" decoding=\"async\" aria-describedby=\"caption-attachment-88\" class=\"size-full wp-image-88\" src=\"https:\/\/fei-lab.org\/wp-content\/uploads\/2017\/03\/D2_Reg_result_fig.jpg\" alt=\"\" width=\"497\" height=\"450\" srcset=\"https:\/\/fei-lab.org\/wp-content\/uploads\/2017\/03\/D2_Reg_result_fig.jpg 497w, https:\/\/fei-lab.org\/wp-content\/uploads\/2017\/03\/D2_Reg_result_fig-300x272.jpg 300w, https:\/\/fei-lab.org\/wp-content\/uploads\/2017\/03\/D2_Reg_result_fig-450x407.jpg 450w\" sizes=\"auto, (max-width: 497px) 100vw, 497px\" \/><p id=\"caption-attachment-88\" class=\"wp-caption-text\">Figure 4. The 2D image registration software.<\/p><\/div>\n<\/div><\/div><\/div><\/section>\n<section class='av_toggle_section av-k9m2o-0ab66bd6d38e3ae822a01909d4f1944d'  itemscope=\"itemscope\" itemtype=\"https:\/\/schema.org\/BlogPosting\" itemprop=\"blogPost\" ><div role=\"tablist\" class=\"single_toggle\" data-tags=\"{All} \"  ><p id='toggle-toggle-id-5' data-fake-id='#toggle-id-5' class='toggler  av-title-above av-inherit-font-color hasCustomColor av-inherit-border-color'  itemprop=\"headline\"  role='tab' tabindex='0' aria-controls='toggle-id-5' data-slide-speed=\"200\" data-title=\"Three-Dimensional (3D) Image Registration Software\" data-title-open=\"\" data-aria_collapsed=\"Click to expand: Three-Dimensional (3D) Image Registration Software\" data-aria_expanded=\"Click to collapse: Three-Dimensional (3D) Image Registration Software\">Three-Dimensional (3D) Image Registration Software<span class=\"toggle_icon\"><span class=\"vert_icon\"><\/span><span class=\"hor_icon\"><\/span><\/span><\/p><div id='toggle-id-5' aria-labelledby='toggle-toggle-id-5' role='region' class='toggle_wrap  av-title-above'  ><div class='toggle_content invers-color av-inherit-font-color hasCustomColor av-inherit-border-color'  itemprop=\"text\" ><p>The 3D registration program can align image volumes from CT, PET, MRI, and\/or other imaging modalities. It has the following features: (1) Resize the floating volume according to the reference volume size and resolution so as to keep the same volume resolution in every direction for the two volumes; (2) Automatic registration based on mutual information; (3) Manual registration using 3D translation and rotation; (4) It includes two deformable registration approaches; (5) Displays volume in all directions as well as the fusion results; and (6) Displays the location line in 3D space and easily locates each point in 3D space. The user interface is also very friendly and direct.<\/p>\n<div id=\"attachment_89\" style=\"width: 717px\" class=\"wp-caption aligncenter\"><img loading=\"lazy\" decoding=\"async\" aria-describedby=\"caption-attachment-89\" class=\"size-full wp-image-89\" src=\"https:\/\/fei-lab.org\/wp-content\/uploads\/2017\/03\/D3_Result_Fig.jpg\" alt=\"\" width=\"707\" height=\"486\" srcset=\"https:\/\/fei-lab.org\/wp-content\/uploads\/2017\/03\/D3_Result_Fig.jpg 707w, https:\/\/fei-lab.org\/wp-content\/uploads\/2017\/03\/D3_Result_Fig-300x206.jpg 300w, https:\/\/fei-lab.org\/wp-content\/uploads\/2017\/03\/D3_Result_Fig-705x485.jpg 705w, https:\/\/fei-lab.org\/wp-content\/uploads\/2017\/03\/D3_Result_Fig-450x309.jpg 450w\" sizes=\"auto, (max-width: 707px) 100vw, 707px\" \/><p id=\"caption-attachment-89\" class=\"wp-caption-text\">Figure 5. The 3D image registration software.<\/p><\/div>\n<\/div><\/div><\/div><\/section>\n<section class='av_toggle_section av-etjfk-022d6ce7c580366884ccc2279290c4eb'  itemscope=\"itemscope\" itemtype=\"https:\/\/schema.org\/BlogPosting\" itemprop=\"blogPost\" ><div role=\"tablist\" class=\"single_toggle\" data-tags=\"{All} \"  ><p id='toggle-toggle-id-6' data-fake-id='#toggle-id-6' class='toggler  av-title-above av-inherit-font-color hasCustomColor av-inherit-border-color'  itemprop=\"headline\"  role='tab' tabindex='0' aria-controls='toggle-id-6' data-slide-speed=\"200\" data-title=\"3D to 2D Registration\" data-title-open=\"\" data-aria_collapsed=\"Click to expand: 3D to 2D Registration\" data-aria_expanded=\"Click to collapse: 3D to 2D Registration\">3D to 2D Registration<span class=\"toggle_icon\"><span class=\"vert_icon\"><\/span><span class=\"hor_icon\"><\/span><\/span><\/p><div id='toggle-id-6' aria-labelledby='toggle-toggle-id-6' role='region' class='toggle_wrap  av-title-above'  ><div class='toggle_content invers-color av-inherit-font-color hasCustomColor av-inherit-border-color'  itemprop=\"text\" ><p>This project implements a three-dimensional (3D) to two-dimensional (2D) registration for computed tomography (CT) and dual-energy digital radiography (DR) for the detection of coronary artery calcification. In order to utilize CT as the \u201cgold standard\u201d to evaluate the ability of DR images to detect and localize calcium, we developed an automatic intensity-based 3D-to-2D registration method for 3D CT volumes and 2D DR images. To generate digital rendering radiographs (DRR) from the CT volumes, we developed three projection methods, including Gaussian-weighted projection, threshold-based projection, and average-based projection, were developed. Cross correlation (NCC) and normalized mutual information (NMI) are used as the similarity measurement.<\/p>\n<p>The software program has the following capabilities: (1) Simulate DR images from the reference CT volume at any angle and generate the projection image using Gaussian-weighted projection, threshold-based projection, and average-based projection methods; and (2) Perform registration between the original DR images and the DRR image reconstructed from the CT volume.<\/p>\n<div id=\"attachment_90\" style=\"width: 495px\" class=\"wp-caption aligncenter\"><img loading=\"lazy\" decoding=\"async\" aria-describedby=\"caption-attachment-90\" class=\"size-full wp-image-90\" src=\"https:\/\/fei-lab.org\/wp-content\/uploads\/2017\/03\/D3_D2_Reg_Fig.jpg\" alt=\"\" width=\"485\" height=\"423\" srcset=\"https:\/\/fei-lab.org\/wp-content\/uploads\/2017\/03\/D3_D2_Reg_Fig.jpg 485w, https:\/\/fei-lab.org\/wp-content\/uploads\/2017\/03\/D3_D2_Reg_Fig-300x262.jpg 300w, https:\/\/fei-lab.org\/wp-content\/uploads\/2017\/03\/D3_D2_Reg_Fig-450x392.jpg 450w\" sizes=\"auto, (max-width: 485px) 100vw, 485px\" \/><p id=\"caption-attachment-90\" class=\"wp-caption-text\">Figure 6. Graphic user interface (GUI) for the 3D to 2D registration software.<\/p><\/div>\n<\/div><\/div><\/div><\/section>\n<section class='av_toggle_section av-oy0o-49ff7b77884cf699e241ff6f29c08a33'  itemscope=\"itemscope\" itemtype=\"https:\/\/schema.org\/BlogPosting\" itemprop=\"blogPost\" ><div role=\"tablist\" class=\"single_toggle\" data-tags=\"{All} \"  ><p id='toggle-toggle-id-7' data-fake-id='#toggle-id-7' class='toggler  av-title-above av-inherit-font-color hasCustomColor av-inherit-border-color'  itemprop=\"headline\"  role='tab' tabindex='0' aria-controls='toggle-id-7' data-slide-speed=\"200\" data-title=\"Slice to Volume Registration\" data-title-open=\"\" data-aria_collapsed=\"Click to expand: Slice to Volume Registration\" data-aria_expanded=\"Click to collapse: Slice to Volume Registration\">Slice to Volume Registration<span class=\"toggle_icon\"><span class=\"vert_icon\"><\/span><span class=\"hor_icon\"><\/span><\/span><\/p><div id='toggle-id-7' aria-labelledby='toggle-toggle-id-7' role='region' class='toggle_wrap  av-title-above'  ><div class='toggle_content invers-color av-inherit-font-color hasCustomColor av-inherit-border-color'  itemprop=\"text\" ><p>Slice to volume registration is used to register a two-dimensional image slice to a three-dimensional image volume. In this study, we registered live-time interventional magnetic resonance imaging (iMRI) slices with a previously obtained, high resolution MRI volume which in turn can be registered with a variety of functional images, e.g. PET and SPECT, for tumor targeting. We created and evaluated a slice-to-volume registration algorithm with special features for its potential use in iMRI-guided, radiofrequency (RF) thermal ablation. The algorithm features included a multi-resolution approach, two similarity measures, and automatic restarting in order to avoid local minima. Imaging experiments were performed on volunteers using a conventional diagnostic MR scanner and an interventional MRI system under realistic conditions. Both high-resolution MR volumes and actual iMRI image slices were acquired from the same volunteers. Actual and simulated iMRI images were used to test the dependence of slice-to-volume registration on image noise, coil inhomogeneity, and RF needle artifacts. To quantitatively assess registration, we calculated the mean voxel displacement over a volume of interest between slice-to-volume registration and volume-to-volume registration, which was previously shown to be quite accurate. More than 800 registration experiments were performed. For transverse image slices covering the prostate, the slice-to-volume registration algorithm was 100% successful with an error of &lt; 2 mm, and the average and standard deviation was only 0.4 mm \u00b1 0.2 mm. Visualizations such as combined sector display and contour overlay showed excellent registration of the prostate and other organs throughout the pelvis. Error was greater when an image slice was obtained at other orientations and positions, mostly because of inconsistent image content such as that obtained from variable rectal and bladder filling. These preliminary experiments indicate that MR slice-to-volume registration is sufficiently accurate to be able to aid image-guided therapy.<\/p>\n<div id=\"attachment_91\" style=\"width: 576px\" class=\"wp-caption aligncenter\"><img loading=\"lazy\" decoding=\"async\" aria-describedby=\"caption-attachment-91\" class=\"size-full wp-image-91\" src=\"https:\/\/fei-lab.org\/wp-content\/uploads\/2017\/03\/Slice_Reg_Fig.jpg\" alt=\"\" width=\"566\" height=\"445\" srcset=\"https:\/\/fei-lab.org\/wp-content\/uploads\/2017\/03\/Slice_Reg_Fig.jpg 566w, https:\/\/fei-lab.org\/wp-content\/uploads\/2017\/03\/Slice_Reg_Fig-300x236.jpg 300w, https:\/\/fei-lab.org\/wp-content\/uploads\/2017\/03\/Slice_Reg_Fig-450x354.jpg 450w\" sizes=\"auto, (max-width: 566px) 100vw, 566px\" \/><p id=\"caption-attachment-91\" class=\"wp-caption-text\">Figure 7. Slice to Volume Registration<\/p><\/div>\n<\/div><\/div><\/div><\/section>\n<\/div>\n\n<style type=\"text\/css\" data-created_by=\"avia_inline_auto\" id=\"style-css-av-1597k-8d788005b04e3da45a2f36dc2f6996e0\">\n#top .hr.hr-invisible.av-1597k-8d788005b04e3da45a2f36dc2f6996e0{\nheight:10px;\n}\n<\/style>\n<div  class='hr av-1597k-8d788005b04e3da45a2f36dc2f6996e0 hr-invisible  avia-builder-el-5  el_after_av_toggle_container  el_before_av_heading '><span class='hr-inner '><span class=\"hr-inner-style\"><\/span><\/span><\/div>\n\n<style type=\"text\/css\" data-created_by=\"avia_inline_auto\" id=\"style-css-av-av_heading-c1bf589944dc6f643abc78f23b6c57a6\">\n#top .av-special-heading.av-av_heading-c1bf589944dc6f643abc78f23b6c57a6{\nmargin:20px 0 0 0;\npadding-bottom:10px;\n}\nbody .av-special-heading.av-av_heading-c1bf589944dc6f643abc78f23b6c57a6 .av-special-heading-tag .heading-char{\nfont-size:25px;\n}\n.av-special-heading.av-av_heading-c1bf589944dc6f643abc78f23b6c57a6 .av-subheading{\nfont-size:15px;\n}\n<\/style>\n<div  class='av-special-heading av-av_heading-c1bf589944dc6f643abc78f23b6c57a6 av-special-heading-h4  avia-builder-el-6  el_after_av_hr  el_before_av_textblock '><h4 class='av-special-heading-tag '  itemprop=\"headline\"  >Selected Publications<\/h4><div class=\"special-heading-border\"><div class=\"special-heading-inner-border\"><\/div><\/div><\/div>\n<section  class='av_textblock_section av-k5pemddv-d0be6b45269927e407d683c1690a499e '   itemscope=\"itemscope\" itemtype=\"https:\/\/schema.org\/BlogPosting\" itemprop=\"blogPost\" ><div class='avia_textblock'  itemprop=\"text\" >\r\n<style>\r\n\tp {display: inline;}\r\n<\/style>\r\n<ul>\r\n\r\n<li>\r\n\r\n<p>Ma L, Rathgeb A, Mubarak H, Tran M, <strong>Fei B<\/strong>. Unsupervised super-resolution reconstruction of hyperspectral histology images for whole-slide imaging. Journal of biomedical optics. 2022 May 1;27(5):056502-.<\/p>\n \r\n\r\n\r\n\r\n\r\nMa_2022_JBO_SuperRes_Reconstruction_WSI\r\n\r\n\t\r\n\r\n<span class=\"pdf\">[<a target=\"_blank\" href=\"https:\/\/www.ncbi.nlm.nih.gov\/pubmed\/35578386\" rel=\"noopener noreferrer\">PubMed<\/a>]<\/span>\r\n\r\n\r\n\t<span class=\"pdf\">[<a target=\"_blank\" href=\"https:\/\/fei-lab.org\/wp-content\/uploads\/2023\/02\/Ma_2022_JBO_SuperRes_Reconstruction_WSI.pdf\" rel=\"noopener noreferrer\">PDF<\/a>]<\/span>\r\n\r\n\r\n\t[<a target=\"_blank\" href=\"https:\/\/doi.org\/10.1117\/1.JBO.27.5.059801\" rel=\"noopener noreferrer\">DOI<\/a>]\r\n\r\n\r\n\t[<a download target=\"_blank\" href=\"https:\/\/fei-lab.org\/wp-content\/uploads\/2023\/02\/Ma_2022_JBO_SuperRes_Reconstruction_WSI_BibTeX.txt\" rel=\"noopener noreferrer\">Bibtex<\/a>]\r\n\r\n\r\n\t[<a download target=\"_blank\" href=\"https:\/\/fei-lab.org\/wp-content\/uploads\/2023\/02\/Ma_2022_JBO_SuperRes_Reconstruction_WSI_EndNote.enw\" rel=\"noopener noreferrer\">Endnote<\/a>]\r\n\r\n\r\n\r\n<div style=\"height: 10px; clear: both;\"><\/div>\t\t\r\n\t<\/li>\r\n\r\n<li>\r\n\r\n<p>Zhou X, Ma L, Mubarak HK, Little JV, Chen AY, Myers LL, Sumer BD, <strong>Fei B<\/strong>. Automatic detection of head and neck squamous cell carcinoma on pathologic slides using polarized hyperspectral imaging and deep learning. In Medical Imaging 2022: Digital and Computational Pathology 2022 Apr 4 (Vol. 12039, pp. 91-100). SPIE.<\/p>\n \r\n\r\n\r\n\r\n\r\nFei_2022_SPIE_Ximing_PHSI_Histology\r\n\r\n\t\r\n\r\n<span class=\"pdf\">[<a target=\"_blank\" href=\"https:\/\/www.ncbi.nlm.nih.gov\/pubmed\/34955584\" rel=\"noopener noreferrer\">PubMed<\/a>]<\/span>\r\n\r\n\r\n\t<span class=\"pdf\">[<a target=\"_blank\" href=\"https:\/\/fei-lab.org\/wp-content\/uploads\/2022\/05\/Fei_2022_SPIE_Ximing_PHSI_Histology.pdf\" rel=\"noopener noreferrer\">PDF<\/a>]<\/span>\r\n\r\n\r\n\t[<a target=\"_blank\" href=\"https:\/\/doi.org\/10.1117\/12.2614624\" rel=\"noopener noreferrer\">DOI<\/a>]\r\n\r\n\r\n\t[<a download target=\"_blank\" href=\"https:\/\/fei-lab.org\/wp-content\/uploads\/2022\/05\/Fei_2022_SPIE_Ximing_PHSI_Histology_BibTeX.txt\" rel=\"noopener noreferrer\">Bibtex<\/a>]\r\n\r\n\r\n\t[<a download target=\"_blank\" href=\"https:\/\/fei-lab.org\/wp-content\/uploads\/2022\/05\/Fei_2022_SPIE_Ximing_PHSI_Histology_EndNote.enw\" rel=\"noopener noreferrer\">Endnote<\/a>]\r\n\r\n\r\n\r\n<div style=\"height: 10px; clear: both;\"><\/div>\t\t\r\n\t<\/li>\r\n\r\n<li>\r\n\r\n<p>Pathour T, Akter N, Dormer JD, Chaudhary S, <strong>Fei B<\/strong>, Sirsi S. Identifying unique acoustic signatures from chemically-crosslinked microbubble clusters using deep learning. In Medical Imaging 2022: Ultrasonic Imaging and Tomography 2022 Apr 4 (Vol. 12038, pp. 128-136). SPIE.<\/p>\n \r\n\r\n\r\n\r\n\r\nFei_2022_SPIE_Teja_Ultrasound_DL\r\n\r\n\t\r\n\r\n<span class=\"pdf\">[<a target=\"_blank\" href=\"https:\/\/www.ncbi.nlm.nih.gov\/pubmed\/36793945\" rel=\"noopener noreferrer\">PubMed<\/a>]<\/span>\r\n\r\n\r\n\t<span class=\"pdf\">[<a target=\"_blank\" href=\"https:\/\/fei-lab.org\/wp-content\/uploads\/2022\/05\/Fei_2022_SPIE_Teja_Ultrasound_DL.pdf\" rel=\"noopener noreferrer\">PDF<\/a>]<\/span>\r\n\r\n\r\n\t[<a target=\"_blank\" href=\"https:\/\/doi.org\/10.1117\/12.2611572\" rel=\"noopener noreferrer\">DOI<\/a>]\r\n\r\n\r\n\t[<a download target=\"_blank\" href=\"https:\/\/fei-lab.org\/wp-content\/uploads\/2022\/05\/Fei_2022_SPIE_Teja_Ultrasound_DL_BibTeX.txt\" rel=\"noopener noreferrer\">Bibtex<\/a>]\r\n\r\n\r\n\t[<a download target=\"_blank\" href=\"https:\/\/fei-lab.org\/wp-content\/uploads\/2022\/05\/Fei_2022_SPIE_Teja_Ultrasound_DL_EndNote.enw\" rel=\"noopener noreferrer\">Endnote<\/a>]\r\n\r\n\r\n\r\n<div style=\"height: 10px; clear: both;\"><\/div>\t\t\r\n\t<\/li>\r\n\r\n<li>\r\n\r\n<p>Leitch K, Shahedi M, Dormer JD, Do QN, Xi Y, Lewis MA, Herrera CL, Spong CY, Madhuranthakam AJ, Twickler DM, <strong>Fei B<\/strong>. Placenta accreta spectrum and hysterectomy prediction using MRI radiomic features. In Medical Imaging 2022: Computer-Aided Diagnosis 2022 Apr 4 (Vol. 12033, pp. 394-399). SPIE.<\/p>\n \r\n\r\n\r\n\r\n\r\nFei_2022_SPIE_KaToria_Placenta_Radiomics\r\n\r\n\t\r\n\r\n<span class=\"pdf\">[<a target=\"_blank\" href=\"https:\/\/www.ncbi.nlm.nih.gov\/pubmed\/36844110\" rel=\"noopener noreferrer\">PubMed<\/a>]<\/span>\r\n\r\n\r\n\t<span class=\"pdf\">[<a target=\"_blank\" href=\"https:\/\/fei-lab.org\/wp-content\/uploads\/2022\/05\/Fei_2022_SPIE_KaToria_Placenta_Radiomics.pdf\" rel=\"noopener noreferrer\">PDF<\/a>]<\/span>\r\n\r\n\r\n\t[<a target=\"_blank\" href=\"https:\/\/doi.org\/10.1117\/12.2611587\" rel=\"noopener noreferrer\">DOI<\/a>]\r\n\r\n\r\n\t[<a download target=\"_blank\" href=\"https:\/\/fei-lab.org\/wp-content\/uploads\/2022\/05\/Fei_2022_SPIE_KaToria_Placenta_Radiomics_BibTeX.txt\" rel=\"noopener noreferrer\">Bibtex<\/a>]\r\n\r\n\r\n\t[<a download target=\"_blank\" href=\"https:\/\/fei-lab.org\/wp-content\/uploads\/2022\/05\/Fei_2022_SPIE_KaToria_Placenta_Radiomics_EndNote.enw\" rel=\"noopener noreferrer\">Endnote<\/a>]\r\n\r\n\r\n\r\n<div style=\"height: 10px; clear: both;\"><\/div>\t\t\r\n\t<\/li>\r\n\r\n<li>\r\n\r\n<p>Leitch K, Halicek M, Shahedi M, Little JV, Chen AY, <strong>Fei B<\/strong>. Detecting aggressive papillary thyroid carcinoma using hyperspectral imaging and radiomic features. In Medical Imaging 2022: Computer-Aided Diagnosis 2022 Apr 4 (Vol. 12033, pp. 537-544). SPIE.<\/p>\n \r\n\r\n\r\n\r\n\r\nFei_2022_SPIE_KaToria_HSI_Thyroid_Radiomics\r\n\r\n\t\r\n\r\n<span class=\"pdf\">[<a target=\"_blank\" href=\"https:\/\/www.ncbi.nlm.nih.gov\/pubmed\/30220773\" rel=\"noopener noreferrer\">PubMed<\/a>]<\/span>\r\n\r\n\r\n\t<span class=\"pdf\">[<a target=\"_blank\" href=\"https:\/\/fei-lab.org\/wp-content\/uploads\/2022\/05\/Fei_2022_SPIE_KaToria_HSI_Thyroid_Radiomics.pdf\" rel=\"noopener noreferrer\">PDF<\/a>]<\/span>\r\n\r\n\r\n\t[<a target=\"_blank\" href=\"https:\/\/doi.org\/10.1117\/12.2611842\" rel=\"noopener noreferrer\">DOI<\/a>]\r\n\r\n\r\n\t[<a download target=\"_blank\" href=\"https:\/\/fei-lab.org\/wp-content\/uploads\/2022\/05\/Fei_2022_SPIE_KaToria_HSI_Thyroid_Radiomics_BibTeX.txt\" rel=\"noopener noreferrer\">Bibtex<\/a>]\r\n\r\n\r\n\t[<a download target=\"_blank\" href=\"https:\/\/fei-lab.org\/wp-content\/uploads\/2022\/05\/Fei_2022_SPIE_KaToria_HSI_Thyroid_Radiomics_EndNote.enw\" rel=\"noopener noreferrer\">Endnote<\/a>]\r\n\r\n\r\n\r\n<div style=\"height: 10px; clear: both;\"><\/div>\t\t\r\n\t<\/li>\r\n\r\n<li>\r\n\r\n<p>Huang J, Guo J, Pedrosa I, <strong>Fei B<\/strong>. Deep learning-based deformable registration of dynamic contrast enhanced MR images of the kidney. In Medical Imaging 2022: Image-Guided Procedures, Robotic Interventions, and Modeling 2022 Apr 4 (Vol. 12034, pp. 213-222). SPIE.<\/p>\n \r\n\r\n\r\n\r\n\r\nFei_2022_SPIE_Huang_Kidney_Segmentation\r\n\r\n\t\r\n\r\n<span class=\"pdf\">[<a target=\"_blank\" href=\"https:\/\/www.ncbi.nlm.nih.gov\/pubmed\/36793654\" rel=\"noopener noreferrer\">PubMed<\/a>]<\/span>\r\n\r\n\r\n\t<span class=\"pdf\">[<a target=\"_blank\" href=\"https:\/\/fei-lab.org\/wp-content\/uploads\/2022\/05\/Fei_2022_SPIE_Huang_Kidney_Segmentation.pdf\" rel=\"noopener noreferrer\">PDF<\/a>]<\/span>\r\n\r\n\r\n\t[<a target=\"_blank\" href=\"https:\/\/doi.org\/10.1117\/12.2611768\" rel=\"noopener noreferrer\">DOI<\/a>]\r\n\r\n\r\n\t[<a download target=\"_blank\" href=\"https:\/\/fei-lab.org\/wp-content\/uploads\/2022\/05\/Fei_2022_SPIE_Huang_Kidney_Segmentation_BibTeX.txt\" rel=\"noopener noreferrer\">Bibtex<\/a>]\r\n\r\n\r\n\t[<a download target=\"_blank\" href=\"https:\/\/fei-lab.org\/wp-content\/uploads\/2022\/05\/Fei_2022_SPIE_Huang_Kidney_Segmentation_EndNote.enw\" rel=\"noopener noreferrer\">Endnote<\/a>]\r\n\r\n\r\n\r\n<div style=\"height: 10px; clear: both;\"><\/div>\t\t\r\n\t<\/li>\r\n\r\n<li>\r\n\r\n<p>Dormer JD, Villordon M, Shahedi M, Do QN, Xi Y, Lewis MA, Madhuranthakam AJ, Herrera CL, Spong CY, Twickler DM, <strong>Fei B<\/strong>. CascadeNet for hysterectomy prediction in pregnant women due to placenta accreta spectrum. In Medical Imaging 2022: Image Processing 2022 Apr 4 (Vol. 12032, pp. 156-164). SPIE.<\/p>\n \r\n\r\n\r\n\r\n\r\nFei_2022_SPIE_Dormer_Placenta_Cascade_Net\r\n\r\n\t\r\n\r\n<span class=\"pdf\">[<a target=\"_blank\" href=\"https:\/\/www.ncbi.nlm.nih.gov\/pubmed\/25426271\" rel=\"noopener noreferrer\">PubMed<\/a>]<\/span>\r\n\r\n\r\n\t<span class=\"pdf\">[<a target=\"_blank\" href=\"https:\/\/fei-lab.org\/wp-content\/uploads\/2022\/05\/Fei_2022_SPIE_Dormer_Placenta_Cascade_Net.pdf\" rel=\"noopener noreferrer\">PDF<\/a>]<\/span>\r\n\r\n\r\n\t[<a target=\"_blank\" href=\"https:\/\/doi.org\/10.1117\/12.2611580\" rel=\"noopener noreferrer\">DOI<\/a>]\r\n\r\n\r\n\t[<a download target=\"_blank\" href=\"https:\/\/fei-lab.org\/wp-content\/uploads\/2022\/05\/Fei_2022_SPIE_Dormer_Placenta_Cascade_Net_BibTeX.txt\" rel=\"noopener noreferrer\">Bibtex<\/a>]\r\n\r\n\r\n\t[<a download target=\"_blank\" href=\"https:\/\/fei-lab.org\/wp-content\/uploads\/2022\/05\/Fei_2022_SPIE_Dormer_Placenta_Cascade_Net_EndNote.enw\" rel=\"noopener noreferrer\">Endnote<\/a>]\r\n\r\n\r\n\r\n<div style=\"height: 10px; clear: both;\"><\/div>\t\t\r\n\t<\/li>\r\n\r\n<li>\r\n\r\n<p>Ma L, Little JV, Chen AY, Myers L, Sumer BD, <strong>Fei B<\/strong>. Automatic detection of head and neck squamous cell carcinoma on histologic slides using hyperspectral microscopic imaging. Journal of Biomedical Optics. 2022 Apr;27(4):046501.<\/p>\n \r\n\r\n\r\n\r\n\r\nFei_2022_Ling_JBO_HSI_Histology\r\n\r\n\t\r\n\r\n<span class=\"pdf\">[<a target=\"_blank\" href=\"https:\/\/www.ncbi.nlm.nih.gov\/pubmed\/35484692\" rel=\"noopener noreferrer\">PubMed<\/a>]<\/span>\r\n\r\n\r\n\t<span class=\"pdf\">[<a target=\"_blank\" href=\"https:\/\/fei-lab.org\/wp-content\/uploads\/2022\/05\/Fei_2022_Ling_JBO_HSI_Histology.pdf\" rel=\"noopener noreferrer\">PDF<\/a>]<\/span>\r\n\r\n\r\n\t[<a target=\"_blank\" href=\"https:\/\/doi.org\/10.1117\/1.JBO.27.5.059802\" rel=\"noopener noreferrer\">DOI<\/a>]\r\n\r\n\r\n\t[<a download target=\"_blank\" href=\"https:\/\/fei-lab.org\/wp-content\/uploads\/2022\/04\/Fei_2022_Ling_JBO_HSI_Histology_BibTeX.txt\" rel=\"noopener noreferrer\">Bibtex<\/a>]\r\n\r\n\r\n\t[<a download target=\"_blank\" href=\"https:\/\/fei-lab.org\/wp-content\/uploads\/2022\/04\/Fei_2022_Ling_JBO_HSI_Histology_EndNote.enw\" rel=\"noopener noreferrer\">Endnote<\/a>]\r\n\r\n\r\n\r\n<div style=\"height: 10px; clear: both;\"><\/div>\t\t\r\n\t<\/li>\r\n\r\n<li>\r\n\r\n<p>Ayala L, Isensee F, Wirkert SJ, Vemuri AS, Maier-Hein KH, <strong>Fei B<\/strong>, Maier-Hein L. Band selection for oxygenation estimation with multispectral\/hyperspectral imaging. Biomedical Optics Express. 2022 Mar 1;13(3):1224-42.<\/p>\n \r\n\r\n\r\n\r\n\r\nFei_2022_BOE_Band_Selection\r\n\r\n\t\r\n\r\n<span class=\"pdf\">[<a target=\"_blank\" href=\"https:\/\/www.ncbi.nlm.nih.gov\/pubmed\/35414995\" rel=\"noopener noreferrer\">PubMed<\/a>]<\/span>\r\n\r\n\r\n\t<span class=\"pdf\">[<a target=\"_blank\" href=\"https:\/\/fei-lab.org\/wp-content\/uploads\/2022\/05\/Fei_2022_BOE_Band_Selection.pdf\" rel=\"noopener noreferrer\">PDF<\/a>]<\/span>\r\n\r\n\r\n\t[<a target=\"_blank\" href=\"https:\/\/doi.org\/10.1364\/BOE.441214\" rel=\"noopener noreferrer\">DOI<\/a>]\r\n\r\n\r\n\t[<a download target=\"_blank\" href=\"https:\/\/fei-lab.org\/wp-content\/uploads\/2022\/05\/Fei_2022_BOE_Band_Selection_BibTeX.txt\" rel=\"noopener noreferrer\">Bibtex<\/a>]\r\n\r\n\r\n\t[<a download target=\"_blank\" href=\"https:\/\/fei-lab.org\/wp-content\/uploads\/2022\/05\/Fei_2022_BOE_Band_Selection_EndNote.enw\" rel=\"noopener noreferrer\">Endnote<\/a>]\r\n\r\n\r\n\r\n<div style=\"height: 10px; clear: both;\"><\/div>\t\t\r\n\t<\/li>\r\n\r\n<li>\r\n\r\n<p>Mehta R, Filos A, Baid U, Sako C, McKinley R, Rebsamen M, D\u00e4twyler K, Meier R, Radojewski P, Murugesan GK, Nalawade S, <strong>Fei B<\/strong>. QU-BraTS: MICCAI BraTS 2020 Challenge on Quantifying Uncertainty in Brain Tumor Segmentation-Analysis of Ranking Scores and Benchmarking Results. Journal of Machine Learning for Biomedical Imaging. 2022;1.<\/p>\n \r\n\r\n\r\n\r\n\r\nMehta_2022_MICCAI\r\n\r\n\t\r\n\r\n<span class=\"pdf\">[<a target=\"_blank\" href=\"https:\/\/www.ncbi.nlm.nih.gov\/pubmed\/36998700\" rel=\"noopener noreferrer\">PubMed<\/a>]<\/span>\r\n\r\n\r\n\t<span class=\"pdf\">[<a target=\"_blank\" href=\"https:\/\/fei-lab.org\/wp-content\/uploads\/2023\/02\/Mehta_2022_MICCAI.pdf\" rel=\"noopener noreferrer\">PDF<\/a>]<\/span>\r\n\r\n\r\n\r\n\t[<a download target=\"_blank\" href=\"https:\/\/fei-lab.org\/wp-content\/uploads\/2023\/02\/Mehta_2022_MICCAI_BibTeX.txt\" rel=\"noopener noreferrer\">Bibtex<\/a>]\r\n\r\n\r\n\t[<a download target=\"_blank\" href=\"https:\/\/fei-lab.org\/wp-content\/uploads\/2023\/02\/Mehta_2022_MICCAI_EndNote.enw\" rel=\"noopener noreferrer\">Endnote<\/a>]\r\n\r\n\r\n\r\n<div style=\"height: 10px; clear: both;\"><\/div>\t\t\r\n\t<\/li>\r\n\r\n<li>\r\n\r\n<p>Shahedi M, Dormer JD, Halicek M, <strong>Fei B<\/strong>. The effect of image annotation with minimal manual interaction for semiautomatic prostate segmentation in CT images using fully convolutional neural networks. Medical physics. 2022 Feb;49(2):1153-60.<\/p>\n \r\n\r\n\r\n\r\n\r\nFei_2021_MP_Maysam_CT_Segmentation\r\n\r\n\t\r\n\r\n\r\n\t<span class=\"pdf\">[<a target=\"_blank\" href=\"https:\/\/fei-lab.org\/wp-content\/uploads\/2022\/05\/Fei_2021_MP_Maysam_CT_Segmentation.pdf\" rel=\"noopener noreferrer\">PDF<\/a>]<\/span>\r\n\r\n\r\n\r\n\t[<a download target=\"_blank\" href=\"https:\/\/fei-lab.org\/wp-content\/uploads\/2022\/05\/Fei_2021_MP_Maysam_CT_Segmentation_BibTeX.txt\" rel=\"noopener noreferrer\">Bibtex<\/a>]\r\n\r\n\r\n\t[<a download target=\"_blank\" href=\"https:\/\/fei-lab.org\/wp-content\/uploads\/2022\/05\/Fei_2021_MP_Maysam_CT_Segmentation_EndNote.enw\" rel=\"noopener noreferrer\">Endnote<\/a>]\r\n\r\n\r\n\r\n<div style=\"height: 10px; clear: both;\"><\/div>\t\t\r\n\t<\/li>\r\n\r\n<li>\r\n\r\n<p>Nalawade SS, Fang FY, Yogananda CG, Murugesan GK, Shah BR, Pinho MC, Wagner BC, Xi Y, Mickey B, Patel TR,<strong> Fei B<\/strong>. Brain tumor IDH, 1p\/19q, and MGMT molecular classification using MRI-based deep learning: an initial study on the effect of motion and motion correction. Journal of Medical Imaging. 2022 Jan;9(1):016001.<\/p>\n \r\n\r\n\r\n\r\n\r\nFei_2022_JMI_Brain_MRI\r\n\r\n\t\r\n\r\n<span class=\"pdf\">[<a target=\"_blank\" href=\"https:\/\/www.ncbi.nlm.nih.gov\/pubmed\/24236230\" rel=\"noopener noreferrer\">PubMed<\/a>]<\/span>\r\n\r\n\r\n\t<span class=\"pdf\">[<a target=\"_blank\" href=\"https:\/\/fei-lab.org\/wp-content\/uploads\/2022\/05\/Fei_2022_JMI_Brain_MRI.pdf\" rel=\"noopener noreferrer\">PDF<\/a>]<\/span>\r\n\r\n\r\n\t[<a target=\"_blank\" href=\"https:\/\/doi.org\/10.1117\/1.JMI.9.1.016001\" rel=\"noopener noreferrer\">DOI<\/a>]\r\n\r\n\r\n\t[<a download target=\"_blank\" href=\"https:\/\/fei-lab.org\/wp-content\/uploads\/2022\/05\/Fei_2022_JMI_Brain_MRI_BibTeX.txt\" rel=\"noopener noreferrer\">Bibtex<\/a>]\r\n\r\n\r\n\t[<a download target=\"_blank\" href=\"https:\/\/fei-lab.org\/wp-content\/uploads\/2022\/05\/Fei_2022_JMI_Brain_MRI_EndNote.enw\" rel=\"noopener noreferrer\">Endnote<\/a>]\r\n\r\n\r\n\r\n<div style=\"height: 10px; clear: both;\"><\/div>\t\t\r\n\t<\/li>\r\n\r\n<li>\r\n\r\n<p>Selim M, Zhang J, <strong>Fei B<\/strong>, Zhang GQ, Chen J. Ct image harmonization for enhancing radiomics studies. In2021 IEEE International Conference on Bioinformatics and Biomedicine (BIBM) 2021 Dec 9 (pp. 1057-1062). IEEE.<\/p>\n \r\n\r\n\r\n\r\n\r\nFei_2021_IEEE_CTImageHarmonization_Selim\r\n\r\n\t\r\n\r\n\r\n\t<span class=\"pdf\">[<a target=\"_blank\" href=\"https:\/\/fei-lab.org\/wp-content\/uploads\/2022\/05\/Fei_2021_IEEE_CTImageHarmonization_Selim.pdf\" rel=\"noopener noreferrer\">PDF<\/a>]<\/span>\r\n\r\n\r\n\r\n\t[<a download target=\"_blank\" href=\"https:\/\/fei-lab.org\/wp-content\/uploads\/2022\/05\/Fei_2021_IEEE_CTImageHarmonization_Selim_BibTeX.txt\" rel=\"noopener noreferrer\">Bibtex<\/a>]\r\n\r\n\r\n\t[<a download target=\"_blank\" href=\"https:\/\/fei-lab.org\/wp-content\/uploads\/2022\/05\/Fei_2021_IEEE_CTImageHarmonization_Selim_EndNote.enw\" rel=\"noopener noreferrer\">Endnote<\/a>]\r\n\r\n\r\n\r\n<div style=\"height: 10px; clear: both;\"><\/div>\t\t\r\n\t<\/li>\r\n\r\n<li>\r\n\r\n<p>Selim M, Zhang J, <strong>Fei B<\/strong>, Zhang GQ, Ge GY, Chen J. Cross-Vendor CT Image Data Harmonization Using CVH-CT. arXiv preprint arXiv:2110.09693. 2021 Oct 19.<\/p>\n \r\n\r\n\r\n\r\n\r\nFei_2021_CycleGAN_AMIA_2021_final\r\n\r\n\t\r\n\r\n<span class=\"pdf\">[<a target=\"_blank\" href=\"https:\/\/www.ncbi.nlm.nih.gov\/pubmed\/32528218\" rel=\"noopener noreferrer\">PubMed<\/a>]<\/span>\r\n\r\n\r\n\t<span class=\"pdf\">[<a target=\"_blank\" href=\"https:\/\/fei-lab.org\/wp-content\/uploads\/2021\/10\/Fei_2021_CycleGAN_AMIA_2021_final.pdf\" rel=\"noopener noreferrer\">PDF<\/a>]<\/span>\r\n\r\n\r\n\r\n\t[<a download target=\"_blank\" href=\"https:\/\/fei-lab.org\/wp-content\/uploads\/2021\/10\/Fei_2021_CycleGAN_AMIA_2021_final_BibTeX.txt\" rel=\"noopener noreferrer\">Bibtex<\/a>]\r\n\r\n\r\n\t[<a download target=\"_blank\" href=\"https:\/\/fei-lab.org\/wp-content\/uploads\/2021\/10\/Fei_2021_CycleGAN_AMIA_2021_final_EndNote.enw\" rel=\"noopener noreferrer\">Endnote<\/a>]\r\n\r\n\r\n\r\n<div style=\"height: 10px; clear: both;\"><\/div>\t\t\r\n\t<\/li>\r\n\r\n<li>\r\n\r\n<p>Shabestri B, Anastasio MA, <strong>Fei B<\/strong>, Leblond F. Special Series Guest Editorial: Artificial Intelligence and Machine Learning in Biomedical Optics. Journal of biomedical optics. 2021 May;26(5).<\/p>\n \r\n\r\n\r\n\r\n\r\nFei_2021_JBO_Editorial_AI\r\n\r\n\t\r\n\r\n<span class=\"pdf\">[<a target=\"_blank\" href=\"https:\/\/www.ncbi.nlm.nih.gov\/pubmed\/33973425\" rel=\"noopener noreferrer\">PubMed<\/a>]<\/span>\r\n\r\n\r\n\t<span class=\"pdf\">[<a target=\"_blank\" href=\"https:\/\/fei-lab.org\/wp-content\/uploads\/2021\/05\/Fei_2021_JBO_Editorial_AI.pdf\" rel=\"noopener noreferrer\">PDF<\/a>]<\/span>\r\n\r\n\r\n\t[<a target=\"_blank\" href=\"https:\/\/doi.org\/10.1117\/1.JBO.26.5.052901\" rel=\"noopener noreferrer\">DOI<\/a>]\r\n\r\n\r\n\t[<a download target=\"_blank\" href=\"https:\/\/fei-lab.org\/wp-content\/uploads\/2021\/05\/Fei_2021_JBO_Editorial_AI_BibTeX.txt\" rel=\"noopener noreferrer\">Bibtex<\/a>]\r\n\r\n\r\n\t[<a download target=\"_blank\" href=\"https:\/\/fei-lab.org\/wp-content\/uploads\/2021\/05\/Fei_2021_JBO_Editorial_AI_EndNote.enw\" rel=\"noopener noreferrer\">Endnote<\/a>]\r\n\r\n\r\n\r\n<div style=\"height: 10px; clear: both;\"><\/div>\t\t\r\n\t<\/li>\r\n\r\n<li>\r\n\r\n<p>Yogananda CG, Shah BR, Nalawade SS, Murugesan GK, Yu FF, Pinho MC, Wagner BC, Mickey B, Patel TR,<strong> Fei B<\/strong>, Madhuranthakam AJ. MRI-based deep-learning method for determining glioma MGMT promoter methylation status. American Journal of Neuroradiology. 2021 May 1;42(5):845-52.<\/p>\n \r\n\r\n\r\n\r\n\r\nFei_2021_GLIOMA\r\n\r\n\t\r\n\r\n\r\n\t<span class=\"pdf\">[<a target=\"_blank\" href=\"https:\/\/fei-lab.org\/wp-content\/uploads\/2022\/05\/Fei_2021_GLIOMA.pdf\" rel=\"noopener noreferrer\">PDF<\/a>]<\/span>\r\n\r\n\r\n\r\n\t[<a download target=\"_blank\" href=\"https:\/\/fei-lab.org\/wp-content\/uploads\/2022\/05\/Fei_2021_GLIOMA_BibTeX.txt\" rel=\"noopener noreferrer\">Bibtex<\/a>]\r\n\r\n\r\n\t[<a download target=\"_blank\" href=\"https:\/\/fei-lab.org\/wp-content\/uploads\/2022\/05\/Fei_2021_GLIOMA_EndNote.enw\" rel=\"noopener noreferrer\">Endnote<\/a>]\r\n\r\n\r\n\r\n<div style=\"height: 10px; clear: both;\"><\/div>\t\t\r\n\t<\/li>\r\n\r\n<li>\r\n\r\n<p>Petrick N, Akbar S, Cha KH, Nofech-Mozes S, Sahiner B, Gavrielides MA, Kalpathy-Cramer J, Drukker K, Martel AL, <strong>Fei B, <\/strong>BreastPathQ Challenge Group FT. SPIE-AAPM-NCI BreastPathQ Challenge: an image analysis challenge for quantitative tumor cellularity assessment in breast cancer histology images following neoadjuvant treatment. Journal of Medical Imaging. 2021 May 1;8(3):034501-.<\/p>\n \r\n\r\n\r\n\r\n\r\nPetrick_2021_BreastPathQ\r\n\r\n\t\r\n\r\n\r\n\t<span class=\"pdf\">[<a target=\"_blank\" href=\"https:\/\/fei-lab.org\/wp-content\/uploads\/2023\/02\/Petrick_2021_BreastPathQ.pdf\" rel=\"noopener noreferrer\">PDF<\/a>]<\/span>\r\n\r\n\r\n\r\n\t[<a download target=\"_blank\" href=\"https:\/\/fei-lab.org\/wp-content\/uploads\/2023\/02\/Petrick_2021_BreastPathQ_BibTeX.txt\" rel=\"noopener noreferrer\">Bibtex<\/a>]\r\n\r\n\r\n\t[<a download target=\"_blank\" href=\"https:\/\/fei-lab.org\/wp-content\/uploads\/2023\/02\/Petrick_2021_BreastPathQ_EndNote.enw\" rel=\"noopener noreferrer\">Endnote<\/a>]\r\n\r\n\r\n\r\n<div style=\"height: 10px; clear: both;\"><\/div>\t\t\r\n\t<\/li>\r\n\r\n<li>\r\n\r\n<p>Yociss M, <strong>Fei B<\/strong>. GPU-based simulation of echocardiography volumes using quantitative fiber-angle-to-backscatter measurements. In Society of Photo-Optical Instrumentation Engineers (SPIE) Conference Series 2021 Feb (Vol. 11602, p. 116020U).<\/p>\n \r\n\r\n\r\n\r\n\r\nFei_2021_Yociss_GPU_US\r\n\r\n\t\r\n\r\n<span class=\"pdf\">[<a target=\"_blank\" href=\"https:\/\/www.ncbi.nlm.nih.gov\/pubmed\/35756345\" rel=\"noopener noreferrer\">PubMed<\/a>]<\/span>\r\n\r\n\r\n\t<span class=\"pdf\">[<a target=\"_blank\" href=\"https:\/\/fei-lab.org\/wp-content\/uploads\/2022\/05\/Fei_2021_Yociss_GPU_US.pdf\" rel=\"noopener noreferrer\">PDF<\/a>]<\/span>\r\n\r\n\r\n\t[<a target=\"_blank\" href=\"https:\/\/doi.org\/10.1117\/12.2581962\" rel=\"noopener noreferrer\">DOI<\/a>]\r\n\r\n\r\n\t[<a download target=\"_blank\" href=\"https:\/\/fei-lab.org\/wp-content\/uploads\/2022\/05\/Fei_2021_Yociss_GPU_US_BibTeX.txt\" rel=\"noopener noreferrer\">Bibtex<\/a>]\r\n\r\n\r\n\t[<a download target=\"_blank\" href=\"https:\/\/fei-lab.org\/wp-content\/uploads\/2022\/05\/Fei_2021_Yociss_GPU_US_EndNote.enw\" rel=\"noopener noreferrer\">Endnote<\/a>]\r\n\r\n\r\n\r\n<div style=\"height: 10px; clear: both;\"><\/div>\t\t\r\n\t<\/li>\r\n\r\n<li>\r\n\r\n<p>Wan C, Ma L, Liu X, <strong>Fei B<\/strong>. Computer-aided classification of lung nodules on CT images with expert knowledge. In Medical Imaging 2021: Image-Guided Procedures, Robotic Interventions, and Modeling 2021 Feb 15 (Vol. 11598, p. 115982K). International Society for Optics and Photonics.<\/p>\n \r\n\r\n\r\n\r\n\r\nFei_2021_Lung_CT\r\n\r\n\t\r\n\r\n<span class=\"pdf\">[<a target=\"_blank\" href=\"https:\/\/www.ncbi.nlm.nih.gov\/pubmed\/35781919\" rel=\"noopener noreferrer\">PubMed<\/a>]<\/span>\r\n\r\n\r\n\t<span class=\"pdf\">[<a target=\"_blank\" href=\"https:\/\/fei-lab.org\/wp-content\/uploads\/2022\/05\/Fei_2021_Lung_CT.pdf\" rel=\"noopener noreferrer\">PDF<\/a>]<\/span>\r\n\r\n\r\n\t[<a target=\"_blank\" href=\"https:\/\/doi.org\/10.1117\/12.2581888\" rel=\"noopener noreferrer\">DOI<\/a>]\r\n\r\n\r\n\t[<a download target=\"_blank\" href=\"https:\/\/fei-lab.org\/wp-content\/uploads\/2022\/05\/Fei_2021_Lung_CT_BibTeX.txt\" rel=\"noopener noreferrer\">Bibtex<\/a>]\r\n\r\n\r\n\t[<a download target=\"_blank\" href=\"https:\/\/fei-lab.org\/wp-content\/uploads\/2022\/05\/Fei_2021_Lung_CT_EndNote.enw\" rel=\"noopener noreferrer\">Endnote<\/a>]\r\n\r\n\r\n\r\n<div style=\"height: 10px; clear: both;\"><\/div>\t\t\r\n\t<\/li>\r\n\r\n<li>\r\n\r\n<p>Edwards K, Halicek M, Little JV, Chen AY, <strong>Fei B<\/strong>. Multiparametric radiomics for predicting the aggressiveness of papillary thyroid carcinoma using hyperspectral images. In Medical Imaging 2021: Computer-Aided Diagnosis 2021 Feb 15 (Vol. 11597, p. 1159728). International Society for Optics and Photonics.<\/p>\n \r\n\r\n\r\n\r\n\r\nFei_2021_Edwards_Radiomics_HSI\r\n\r\n\t\r\n\r\n<span class=\"pdf\">[<a target=\"_blank\" href=\"https:\/\/www.ncbi.nlm.nih.gov\/pubmed\/35756897\" rel=\"noopener noreferrer\">PubMed<\/a>]<\/span>\r\n\r\n\r\n\t<span class=\"pdf\">[<a target=\"_blank\" href=\"https:\/\/fei-lab.org\/wp-content\/uploads\/2021\/02\/Fei_2021_Edwards_Radiomics_HSI.pdf\" rel=\"noopener noreferrer\">PDF<\/a>]<\/span>\r\n\r\n\r\n\t[<a target=\"_blank\" href=\"https:\/\/doi.org\/10.1117\/12.2582147\" rel=\"noopener noreferrer\">DOI<\/a>]\r\n\r\n\r\n\t[<a download target=\"_blank\" href=\"https:\/\/fei-lab.org\/wp-content\/uploads\/2021\/02\/Fei_2021_Edwards_Radiomics_HSI_BibTeX.txt\" rel=\"noopener noreferrer\">Bibtex<\/a>]\r\n\r\n\r\n\t[<a download target=\"_blank\" href=\"https:\/\/fei-lab.org\/wp-content\/uploads\/2021\/02\/Fei_2021_Edwards_Radiomics_HSI_EndNote.enw\" rel=\"noopener noreferrer\">Endnote<\/a>]\r\n\r\n\r\n\r\n<div style=\"height: 10px; clear: both;\"><\/div>\t\t\r\n\t<\/li>\r\n\r\n<li>\r\n\r\n<p>Ma L, <strong>Fei B<\/strong>. Comprehensive review of surgical microscopes: technology development and medical applications. Journal of biomedical optics. 2021 Jan;26(1):010901.<\/p>\n \r\n\r\n\r\n\r\n\r\nFei_2021_Ling_Surgical_Microscope_Review\r\n\r\n\t\r\n\r\n<span class=\"pdf\">[<a target=\"_blank\" href=\"https:\/\/www.ncbi.nlm.nih.gov\/pubmed\/33398948\" rel=\"noopener noreferrer\">PubMed<\/a>]<\/span>\r\n\r\n\r\n\t<span class=\"pdf\">[<a target=\"_blank\" href=\"https:\/\/fei-lab.org\/wp-content\/uploads\/2022\/05\/Fei_2021_Ling_Surgical_Microscope_Review.pdf\" rel=\"noopener noreferrer\">PDF<\/a>]<\/span>\r\n\r\n\r\n\t[<a target=\"_blank\" href=\"https:\/\/doi.org\/10.1117\/1.JBO.26.1.010901\" rel=\"noopener noreferrer\">DOI<\/a>]\r\n\r\n\r\n\t[<a download target=\"_blank\" href=\"https:\/\/fei-lab.org\/wp-content\/uploads\/2022\/05\/Fei_2021_Ling_Surgical_Microscope_Review_BibTeX.txt\" rel=\"noopener noreferrer\">Bibtex<\/a>]\r\n\r\n\r\n\t[<a download target=\"_blank\" href=\"https:\/\/fei-lab.org\/wp-content\/uploads\/2022\/05\/Fei_2021_Ling_Surgical_Microscope_Review_EndNote.enw\" rel=\"noopener noreferrer\">Endnote<\/a>]\r\n\r\n\r\n\r\n<div style=\"height: 10px; clear: both;\"><\/div>\t\t\r\n\t<\/li>\r\n\r\n<li>\r\n\r\n<p>Ortega S, Halicek M, Fabelo H, Quevedo E, <strong>Fei B<\/strong>, Callico GM. Information Extraction Techniques in Hyperspectral Imaging Biomedical Applications. InMultimedia Information Retrieval 2020 Oct 10. IntechOpen.<\/p>\n \r\n\r\n\r\n\r\n\r\nFei_2020_HSI_Book_Chapter\r\n\r\n\t\r\n\r\n\r\n\t<span class=\"pdf\">[<a target=\"_blank\" href=\"https:\/\/fei-lab.org\/wp-content\/uploads\/2022\/05\/Fei_2020_HSI_Book_Chapter.pdf\" rel=\"noopener noreferrer\">PDF<\/a>]<\/span>\r\n\r\n\r\n\r\n\t[<a download target=\"_blank\" href=\"https:\/\/fei-lab.org\/wp-content\/uploads\/2022\/05\/Fei_2020_HSI_Book_Chapter_BibTeX.txt\" rel=\"noopener noreferrer\">Bibtex<\/a>]\r\n\r\n\r\n\t[<a download target=\"_blank\" href=\"https:\/\/fei-lab.org\/wp-content\/uploads\/2022\/05\/Fei_2020_HSI_Book_Chapter_EndNote.enw\" rel=\"noopener noreferrer\">Endnote<\/a>]\r\n\r\n\r\n\r\n<div style=\"height: 10px; clear: both;\"><\/div>\t\t\r\n\t<\/li>\r\n\r\n<li>\r\n\r\n<div class=\"gs_citr\" tabindex=\"0\">Yogananda CG, Shah BR, Vejdani-Jahromi M, Nalawade SS, Murugesan GK, Yu FF, Pinho MC, Wagner BC, Emblem KE, Bj\u00f8rnerud A, <strong>Fei BW<\/strong> (Corresponding author). A fully automated deep learning network for brain tumor segmentation. Tomography. 2020 Jun; 6(2):186.<\/div>\n \r\n\r\n\r\n\r\n\r\nYogananda_2020_Tomography\r\n\r\n\t\r\n\r\n<span class=\"pdf\">[<a target=\"_blank\" href=\"https:\/\/www.ncbi.nlm.nih.gov\/pubmed\/32548295\" rel=\"noopener noreferrer\">PubMed<\/a>]<\/span>\r\n\r\n\r\n\t<span class=\"pdf\">[<a target=\"_blank\" href=\"https:\/\/fei-lab.org\/wp-content\/uploads\/2020\/09\/Fei_2020_Yogananda_Tomography_6_2.pdf\" rel=\"noopener noreferrer\">PDF<\/a>]<\/span>\r\n\r\n\r\n\t[<a target=\"_blank\" href=\"https:\/\/doi.org\/10.18383\/j.tom.2019.00026\" rel=\"noopener noreferrer\">DOI<\/a>]\r\n\r\n\r\n\t[<a download target=\"_blank\" href=\"https:\/\/fei-lab.org\/wp-content\/uploads\/2020\/09\/Yogananda_2020_Tomography_BibTeX.txt\" rel=\"noopener noreferrer\">Bibtex<\/a>]\r\n\r\n\r\n\t[<a download target=\"_blank\" href=\"https:\/\/fei-lab.org\/wp-content\/uploads\/2020\/09\/Yogananda_2020_Tomography_EndNote.enw\" rel=\"noopener noreferrer\">Endnote<\/a>]\r\n\r\n\r\n\r\n<div style=\"height: 10px; clear: both;\"><\/div>\t\t\r\n\t<\/li>\r\n\r\n<li>\r\n\r\n<div class=\"gs_citr\" tabindex=\"0\">Ortega S, Halicek M, Fabelo H, Camacho R, Plaza MD, Godtliebsen F, M Callic\u00f3 G, <strong>Fei BW <\/strong>(Corresponding author). Hyperspectral imaging for the detection of glioblastoma tumor cells in H&amp;E slides using convolutional neural networks. Sensors; 20(7):1911.<\/div>\n \r\n\r\n\r\n\r\n\r\nOrtega_2020_Sensors\r\n\r\n\t\r\n\r\n<span class=\"pdf\">[<a target=\"_blank\" href=\"https:\/\/www.ncbi.nlm.nih.gov\/pubmed\/32235483\" rel=\"noopener noreferrer\">PubMed<\/a>]<\/span>\r\n\r\n\r\n\t<span class=\"pdf\">[<a target=\"_blank\" href=\"https:\/\/fei-lab.org\/wp-content\/uploads\/2020\/06\/Fei_2020_Samuel_Sensors_GBM_Cells_HSI.pdf\" rel=\"noopener noreferrer\">PDF<\/a>]<\/span>\r\n\r\n\r\n\t[<a target=\"_blank\" href=\"https:\/\/doi.org\/10.3390\/s20071911\" rel=\"noopener noreferrer\">DOI<\/a>]\r\n\r\n\r\n\t[<a download target=\"_blank\" href=\"https:\/\/fei-lab.org\/wp-content\/uploads\/2020\/06\/Ortega_2020_Sensors_BibTeX.txt\" rel=\"noopener noreferrer\">Bibtex<\/a>]\r\n\r\n\r\n\t[<a download target=\"_blank\" href=\"https:\/\/fei-lab.org\/wp-content\/uploads\/2020\/06\/Ortega_2020_Sensors_EndNote.enw\" rel=\"noopener noreferrer\">Endnote<\/a>]\r\n\r\n\r\n\r\n<div style=\"height: 10px; clear: both;\"><\/div>\t\t\r\n\t<\/li>\r\n\r\n<li>\r\n\r\n<div class=\"gs_citr\" tabindex=\"0\">Nalawade S, Fang FY, Yogananda CG, Murugesan GK, Shah BR, Pinho MC, Wagner BC, Mickey B, Patel TR, <strong>Fei BW<\/strong>, Madhuranthakam AJ, Maldjian JA. Brain tumor IDH, 1p\/19q, and MGMT molecular classification using MRI-based deep learning: effect of motion and motion correction. bioRxiv 2020.<\/div>\n \r\n\r\n\r\n\r\n\r\nNalawade_2020_bioRxiv\r\n\r\n\t\r\n\r\n\r\n\t<span class=\"pdf\">[<a target=\"_blank\" href=\"https:\/\/fei-lab.org\/wp-content\/uploads\/2020\/06\/Fei_2020_bioRxiv_Maldjian_MRI_IDH.pdf\" rel=\"noopener noreferrer\">PDF<\/a>]<\/span>\r\n\r\n\r\n\r\n\t[<a download target=\"_blank\" href=\"https:\/\/fei-lab.org\/wp-content\/uploads\/2020\/06\/Nalawade_2020_bioRxiv_BibTeX.txt\" rel=\"noopener noreferrer\">Bibtex<\/a>]\r\n\r\n\r\n\t[<a download target=\"_blank\" href=\"https:\/\/fei-lab.org\/wp-content\/uploads\/2020\/06\/Nalawade_2020_bioRxiv_EndNote.enw\" rel=\"noopener noreferrer\">Endnote<\/a>]\r\n\r\n\r\n\r\n<div style=\"height: 10px; clear: both;\"><\/div>\t\t\r\n\t<\/li>\r\n\r\n<li>\r\n\r\n<p>Tran CT, Halicek M, Dormer JD, Tandon A, Hussain T, <strong>Fei BW<\/strong> (Corresponding author). Fully automated segmentation of the right ventricle in patients with repaired Tetralogy of Fallot using U-Net. Medical Imaging 2020: Biomedical Applications in Molecular, Structural, and Functional Imaging; 11317(113171M). International Society for Optics and Photonics,<\/p>\n \r\n\r\n\r\n\r\n\r\nTran_2020_SPIE\r\n\r\n\t\r\n\r\n\r\n\t<span class=\"pdf\">[<a target=\"_blank\" href=\"https:\/\/fei-lab.org\/wp-content\/uploads\/2020\/06\/Fei_2020_Heart_Segmentation_113171M.pdf\" rel=\"noopener noreferrer\">PDF<\/a>]<\/span>\r\n\r\n\r\n\t[<a target=\"_blank\" href=\"https:\/\/doi.org\/10.1117\/12.2549052\" rel=\"noopener noreferrer\">DOI<\/a>]\r\n\r\n\r\n\t[<a download target=\"_blank\" href=\"https:\/\/fei-lab.org\/wp-content\/uploads\/2020\/06\/Tran_2020_SPIE_BibTeX.txt\" rel=\"noopener noreferrer\">Bibtex<\/a>]\r\n\r\n\r\n\t[<a download target=\"_blank\" href=\"https:\/\/fei-lab.org\/wp-content\/uploads\/2020\/06\/Tran_2020_SPIE_EndNote.enw\" rel=\"noopener noreferrer\">Endnote<\/a>]\r\n\r\n\r\n\r\n<div style=\"height: 10px; clear: both;\"><\/div>\t\t\r\n\t<\/li>\r\n\r\n<li>\r\n\r\n<div class=\"gs_citr\" tabindex=\"0\">Ma L, Halicek M, <strong>Fei BW<\/strong>. In vivo cancer detection in animal model using hyperspectral image classification with wavelet feature extraction. Medical Imaging 2020: Biomedical Applications in Molecular, Structural, and Functional Imaging; 11317(113171C). International Society for Optics and Photonics,<\/div>\n \r\n\r\n\r\n\r\n\r\nMa_2020_SPIE_2\r\n\r\n\t\r\n\r\n<span class=\"pdf\">[<a target=\"_blank\" href=\"https:\/\/www.ncbi.nlm.nih.gov\/pubmed\/32476705\" rel=\"noopener noreferrer\">PubMed<\/a>]<\/span>\r\n\r\n\r\n\t<span class=\"pdf\">[<a target=\"_blank\" href=\"https:\/\/fei-lab.org\/wp-content\/uploads\/2020\/06\/Fei_2020_LingMa_HSI_113171C.pdf\" rel=\"noopener noreferrer\">PDF<\/a>]<\/span>\r\n\r\n\r\n\t[<a target=\"_blank\" href=\"https:\/\/doi.org\/10.1117\/12.2549397\" rel=\"noopener noreferrer\">DOI<\/a>]\r\n\r\n\r\n\t[<a download target=\"_blank\" href=\"https:\/\/fei-lab.org\/wp-content\/uploads\/2020\/06\/Ma_2020_SPIE_2_BibTeX.txt\" rel=\"noopener noreferrer\">Bibtex<\/a>]\r\n\r\n\r\n\t[<a download target=\"_blank\" href=\"https:\/\/fei-lab.org\/wp-content\/uploads\/2020\/06\/Ma_2020_SPIE_2_EndNote.enw\" rel=\"noopener noreferrer\">Endnote<\/a>]\r\n\r\n\r\n\r\n<div style=\"height: 10px; clear: both;\"><\/div>\t\t\r\n\t<\/li>\r\n\r\n<li>\r\n\r\n<div class=\"gs_citr\" tabindex=\"0\">Edwards K, Chhabra A, Dormer J, Jones P, Boutin RD, Lenchik L, <strong>Fei BW <\/strong>(Corresponding author). Abdominal muscle segmentation from CT using a convolutional neural network. Medical Imaging 2020: Biomedical Applications in Molecular, Structural, and Functional Imaging; 11317(113170L). International Society for Optics and Photonics,<\/div>\n \r\n\r\n\r\n\r\n\r\nEdwards_2020_SPIE\r\n\r\n\t\r\n\r\n<span class=\"pdf\">[<a target=\"_blank\" href=\"https:\/\/www.ncbi.nlm.nih.gov\/pubmed\/32577045\" rel=\"noopener noreferrer\">PubMed<\/a>]<\/span>\r\n\r\n\r\n\t<span class=\"pdf\">[<a target=\"_blank\" href=\"https:\/\/fei-lab.org\/wp-content\/uploads\/2020\/06\/Fei_2020_KaToria_Segmentation_113170L.pdf\" rel=\"noopener noreferrer\">PDF<\/a>]<\/span>\r\n\r\n\r\n\t[<a target=\"_blank\" href=\"https:\/\/doi.org\/10.1117\/12.2549406\" rel=\"noopener noreferrer\">DOI<\/a>]\r\n\r\n\r\n\t[<a download target=\"_blank\" href=\"https:\/\/fei-lab.org\/wp-content\/uploads\/2020\/06\/Edwards_2020_SPIE_BibTeX.txt\" rel=\"noopener noreferrer\">Bibtex<\/a>]\r\n\r\n\r\n\t[<a download target=\"_blank\" href=\"https:\/\/fei-lab.org\/wp-content\/uploads\/2020\/06\/Edwards_2020_SPIE_EndNote.enw\" rel=\"noopener noreferrer\">Endnote<\/a>]\r\n\r\n\r\n\r\n<div style=\"height: 10px; clear: both;\"><\/div>\t\t\r\n\t<\/li>\r\n\r\n<li>\r\n\r\n<div class=\"gs_citr\" tabindex=\"0\">Halicek M, Dormer JD, Little JV, Chen AY, <strong>Fei BW <\/strong>(Corresponding author). Tumor detection of the thyroid and salivary glands using hyperspectral imaging and deep learning. Biomedical Optics Express; 11(3):1383-400.<\/div>\n \r\n\r\n\r\n\r\n\r\nHalicek_2020_BOE_2\r\n\r\n\t\r\n\r\n<span class=\"pdf\">[<a target=\"_blank\" href=\"https:\/\/www.ncbi.nlm.nih.gov\/pubmed\/32206417\" rel=\"noopener noreferrer\">PubMed<\/a>]<\/span>\r\n\r\n\r\n\t<span class=\"pdf\">[<a target=\"_blank\" href=\"https:\/\/fei-lab.org\/wp-content\/uploads\/2020\/06\/Fei_2020_BOE_Halicek_Tumor_detection_HSI.pdf\" rel=\"noopener noreferrer\">PDF<\/a>]<\/span>\r\n\r\n\r\n\t[<a target=\"_blank\" href=\"https:\/\/doi.org\/10.1364\/BOE.381257\" rel=\"noopener noreferrer\">DOI<\/a>]\r\n\r\n\r\n\t[<a download target=\"_blank\" href=\"https:\/\/fei-lab.org\/wp-content\/uploads\/2020\/06\/Halicek_2020_BOE_2_BibTeX.txt\" rel=\"noopener noreferrer\">Bibtex<\/a>]\r\n\r\n\r\n\t[<a download target=\"_blank\" href=\"https:\/\/fei-lab.org\/wp-content\/uploads\/2020\/06\/Halicek_2020_BOE_2_EndNote.enw\" rel=\"noopener noreferrer\">Endnote<\/a>]\r\n\r\n\r\n\r\n<div style=\"height: 10px; clear: both;\"><\/div>\t\t\r\n\t<\/li>\r\n\r\n<li>\r\n\r\n<p>Bangalore Yogananda CG, Shah BR, Vejdani-Jahromi M, Nalawade SS, Murugesan GK, Yu FF, Pinho MC, Wagner BC, Mickey B, Patel TR, <strong>Fei BW <\/strong>(Corresponding author). A novel fully automated MRI-based deep-learning method for classification of IDH mutation status in brain gliomas. Neuro-oncology; 22(3):402-11.<\/p>\n \r\n\r\n\r\n\r\n\r\nYogananda_2020_NeuroOnc\r\n\r\n\t\r\n\r\n<span class=\"pdf\">[<a target=\"_blank\" href=\"https:\/\/www.ncbi.nlm.nih.gov\/pubmed\/31637430\" rel=\"noopener noreferrer\">PubMed<\/a>]<\/span>\r\n\r\n\r\n\t<span class=\"pdf\">[<a target=\"_blank\" href=\"https:\/\/fei-lab.org\/wp-content\/uploads\/2020\/06\/Fei_2020_Neuro-Oncology_MRI_DL_IDH.pdf\" rel=\"noopener noreferrer\">PDF<\/a>]<\/span>\r\n\r\n\r\n\t[<a target=\"_blank\" href=\"https:\/\/doi.org\/10.1093\/neuonc\/noz199\" rel=\"noopener noreferrer\">DOI<\/a>]\r\n\r\n\r\n\t[<a download target=\"_blank\" href=\"https:\/\/fei-lab.org\/wp-content\/uploads\/2020\/06\/Yogananda_2020_NeuroOnc_BibTeX.txt\" rel=\"noopener noreferrer\">Bibtex<\/a>]\r\n\r\n\r\n\t[<a download target=\"_blank\" href=\"https:\/\/fei-lab.org\/wp-content\/uploads\/2020\/06\/Yogananda_2020_NeuroOnc_EndNote.enw\" rel=\"noopener noreferrer\">Endnote<\/a>]\r\n\r\n\r\n\r\n<div style=\"height: 10px; clear: both;\"><\/div>\t\t\r\n\t<\/li>\r\n\r\n<li>\r\n\r\n<p>Shahedi M, Halicek M, Dormer JD, <strong>Fei BW <\/strong>(Corresponding author). Incorporating minimal user input into deep learning based image segmentation. Medical Imaging 2020: Image Processing; 11313(1131313). International Society for Optics and Photonics,<\/p>\n \r\n\r\n\r\n\r\n\r\nShahedi_2020_SPIE_2\r\n\r\n\t\r\n\r\n<span class=\"pdf\">[<a target=\"_blank\" href=\"https:\/\/www.ncbi.nlm.nih.gov\/pubmed\/32476701\" rel=\"noopener noreferrer\">PubMed<\/a>]<\/span>\r\n\r\n\r\n\t<span class=\"pdf\">[<a target=\"_blank\" href=\"https:\/\/fei-lab.org\/wp-content\/uploads\/2020\/06\/Fei_2020_Maysam_Deep_Learning_1131313.pdf\" rel=\"noopener noreferrer\">PDF<\/a>]<\/span>\r\n\r\n\r\n\t[<a target=\"_blank\" href=\"https:\/\/doi.org\/10.1117\/12.2549716\" rel=\"noopener noreferrer\">DOI<\/a>]\r\n\r\n\r\n\t[<a download target=\"_blank\" href=\"https:\/\/fei-lab.org\/wp-content\/uploads\/2020\/06\/Shahedi_2020_SPIE_2_BibTeX.txt\" rel=\"noopener noreferrer\">Bibtex<\/a>]\r\n\r\n\r\n\t[<a download target=\"_blank\" href=\"https:\/\/fei-lab.org\/wp-content\/uploads\/2020\/06\/Shahedi_2020_SPIE_2_EndNote.enw\" rel=\"noopener noreferrer\">Endnote<\/a>]\r\n\r\n\r\n\r\n<div style=\"height: 10px; clear: both;\"><\/div>\t\t\r\n\t<\/li>\r\n\r\n<li>\r\n\r\n<p>Mavuduru A, Halicek M, Shahedi M, Little JV, Chen AY, Myers LL, <strong>Fei BW<\/strong> (Corresponding author). Using a 22-layer U-Net to perform segmentation of squamous cell carcinoma on digitized head and neck histological images. Medical Imaging 2020: Digital Pathology; 11320(113200C). International Society for Optics and Photonics,<\/p>\n \r\n\r\n\r\n\r\n\r\nMavuduru_2020_SPIE\r\n\r\n\t\r\n\r\n<span class=\"pdf\">[<a target=\"_blank\" href=\"https:\/\/www.ncbi.nlm.nih.gov\/pubmed\/32476709\" rel=\"noopener noreferrer\">PubMed<\/a>]<\/span>\r\n\r\n\r\n\t<span class=\"pdf\">[<a target=\"_blank\" href=\"https:\/\/fei-lab.org\/wp-content\/uploads\/2020\/06\/Fei_2020_Amol_Histology_113200C.pdf\" rel=\"noopener noreferrer\">PDF<\/a>]<\/span>\r\n\r\n\r\n\t[<a target=\"_blank\" href=\"https:\/\/doi.org\/10.1117\/12.2549061\" rel=\"noopener noreferrer\">DOI<\/a>]\r\n\r\n\r\n\t[<a download target=\"_blank\" href=\"https:\/\/fei-lab.org\/wp-content\/uploads\/2020\/06\/Mavuduru_2020_SPIE_BibTeX.txt\" rel=\"noopener noreferrer\">Bibtex<\/a>]\r\n\r\n\r\n\t[<a download target=\"_blank\" href=\"https:\/\/fei-lab.org\/wp-content\/uploads\/2020\/06\/Mavuduru_2020_SPIE_EndNote.enw\" rel=\"noopener noreferrer\">Endnote<\/a>]\r\n\r\n\r\n\r\n<div style=\"height: 10px; clear: both;\"><\/div>\t\t\r\n\t<\/li>\r\n\r\n<li>\r\n\r\n<p>Shahedi M, Dormer JD, TT AD, Do QN, Xi Y, Lewis MA, Madhuranthakam AJ, Twickler DM, <strong>Fei BW <\/strong>(Corresponding author). Segmentation of uterus and placenta in MR images using a fully convolutional neural network. Medical Imaging 2020: Computer-Aided Diagnosis; 11314(113141R). International Society for Optics and Photonics,<\/p>\n \r\n\r\n\r\n\r\n\r\nShahedi_2020_SPIE\r\n\r\n\t\r\n\r\n<span class=\"pdf\">[<a target=\"_blank\" href=\"https:\/\/www.ncbi.nlm.nih.gov\/pubmed\/32476702\" rel=\"noopener noreferrer\">PubMed<\/a>]<\/span>\r\n\r\n\r\n\t<span class=\"pdf\">[<a target=\"_blank\" href=\"https:\/\/fei-lab.org\/wp-content\/uploads\/2020\/06\/Fei_2020_Maysam_Placenta_113141R.pdf\" rel=\"noopener noreferrer\">PDF<\/a>]<\/span>\r\n\r\n\r\n\t[<a target=\"_blank\" href=\"https:\/\/doi.org\/10.1117\/12.2549873\" rel=\"noopener noreferrer\">DOI<\/a>]\r\n\r\n\r\n\t[<a download target=\"_blank\" href=\"https:\/\/fei-lab.org\/wp-content\/uploads\/2020\/06\/Shahedi_2020_SPIE_BibTeX.txt\" rel=\"noopener noreferrer\">Bibtex<\/a>]\r\n\r\n\r\n\t[<a download target=\"_blank\" href=\"https:\/\/fei-lab.org\/wp-content\/uploads\/2020\/06\/Shahedi_2020_SPIE_EndNote.enw\" rel=\"noopener noreferrer\">Endnote<\/a>]\r\n\r\n\r\n\r\n<div style=\"height: 10px; clear: both;\"><\/div>\t\t\r\n\t<\/li>\r\n\r\n<li>\r\n\r\n<p>Hao D, Ding S, Qiu L, Lv Y,<strong> Fei BW<\/strong>, Zhu Y, Qin B. Sequential vessel segmentation via deep channel attention network. Neural Networks.<\/p>\n \r\n\r\n\r\n\r\n\r\nHao_2020_NeuralN\r\n\r\n\t\r\n\r\n<span class=\"pdf\">[<a target=\"_blank\" href=\"https:\/\/www.ncbi.nlm.nih.gov\/pubmed\/16039535\" rel=\"noopener noreferrer\">PubMed<\/a>]<\/span>\r\n\r\n\r\n\t<span class=\"pdf\">[<a target=\"_blank\" href=\"https:\/\/fei-lab.org\/wp-content\/uploads\/2020\/06\/Fei_2020_NeuralN_Hao_Sequential_vessel_deep_channel.pdf\" rel=\"noopener noreferrer\">PDF<\/a>]<\/span>\r\n\r\n\r\n\t[<a target=\"_blank\" href=\"https:\/\/doi.org\/10.1016\/j.neunet.2020.05.005\" rel=\"noopener noreferrer\">DOI<\/a>]\r\n\r\n\r\n\t[<a download target=\"_blank\" href=\"https:\/\/fei-lab.org\/wp-content\/uploads\/2020\/06\/Hao_2020_NeuralN_BibTeX.txt\" rel=\"noopener noreferrer\">Bibtex<\/a>]\r\n\r\n\r\n\t[<a download target=\"_blank\" href=\"https:\/\/fei-lab.org\/wp-content\/uploads\/2020\/06\/Hao_2020_NeuralN_EndNote.enw\" rel=\"noopener noreferrer\">Endnote<\/a>]\r\n\r\n\r\n\r\n<div style=\"height: 10px; clear: both;\"><\/div>\t\t\r\n\t<\/li>\r\n\r\n<li>\r\n\r\n<p>Ortega S, Halicek M, Fabelo H, Callico GM, <strong>Fei BW<\/strong><strong>\u00a0<\/strong>(Corresponding author). Hyperspectral and multispectral imaging in digital and computational pathology: a systematic review. Biomedical Optics Express; 11(6): 3195-3233.<\/p>\n \r\n\r\n\r\n\r\n\r\n\t\r\n\r\n<span class=\"pdf\">[<a target=\"_blank\" href=\"https:\/\/www.ncbi.nlm.nih.gov\/pubmed\/32637250\" rel=\"noopener noreferrer\">PubMed<\/a>]<\/span>\r\n\r\n\r\n\t<span class=\"pdf\">[<a target=\"_blank\" href=\"https:\/\/fei-lab.org\/wp-content\/uploads\/2020\/06\/Fei_2020_BOE_Halicek_Hyperspectral_systematic_review.pdf\" rel=\"noopener noreferrer\">PDF<\/a>]<\/span>\r\n\r\n\r\n\t[<a target=\"_blank\" href=\"https:\/\/doi.org\/10.1364\/BOE.386338\" rel=\"noopener noreferrer\">DOI<\/a>]\r\n\r\n\r\n\r\n\r\n\r\n<div style=\"height: 10px; clear: both;\"><\/div>\t\t\r\n\t<\/li>\r\n\r\n<li>\r\n\r\n<p>Fabelo H, Halicek M, Ortega S, Shahedi M, Szolna A, Pineiro JF, Sosa C, O&#8217;Shanahan AJ, Bisshopp S, Espino C, Marquez M, Hernandez M, Carrera D, Morera J, Callic\u00f3 GM, Sarmiento R, <strong>Fei BW<\/strong> (Corresponding author). Deep learning-based framework for in-vivo identification of glioblastoma tumor using hyperspectral images of human brain. Sensors;19(4).<\/p>\n \r\n\r\n\r\n\r\n\r\nFabelo_2019_Sensors\r\n\r\n\t\r\n\r\n<span class=\"pdf\">[<a target=\"_blank\" href=\"https:\/\/www.ncbi.nlm.nih.gov\/pubmed\/30813245\" rel=\"noopener noreferrer\">PubMed<\/a>]<\/span>\r\n\r\n\r\n\t<span class=\"pdf\">[<a target=\"_blank\" href=\"https:\/\/fei-lab.org\/wp-content\/uploads\/2019\/08\/Fei_2019_Sensors_Fabelo_Deep_learning_Framework_Glioblastoma_Hyperspectral_Brain.pdf\" rel=\"noopener noreferrer\">PDF<\/a>]<\/span>\r\n\r\n\r\n\t[<a target=\"_blank\" href=\"https:\/\/doi.org\/10.3390\/s19040920\" rel=\"noopener noreferrer\">DOI<\/a>]\r\n\r\n\r\n\t[<a download target=\"_blank\" href=\"https:\/\/fei-lab.org\/wp-content\/uploads\/2019\/09\/Fabelo_2019_Sensors_BibTeX.txt\" rel=\"noopener noreferrer\">Bibtex<\/a>]\r\n\r\n\r\n\t[<a download target=\"_blank\" href=\"https:\/\/fei-lab.org\/wp-content\/uploads\/2019\/09\/Fabelo_2019_Sensors_EndNote.enw\" rel=\"noopener noreferrer\">Endnote<\/a>]\r\n\r\n\r\n\r\n<div style=\"height: 10px; clear: both;\"><\/div>\t\t\r\n\t<\/li>\r\n\r\n<li>\r\n\r\n<p>Halicek M, Shahedi M, Little JV, Chen AY, Myers LL, Sumer BD, <strong>Fei BW<\/strong>, Tomaszewski JE, Ward AD. Detection of squamous cell carcinoma in digitized histological images from the head and neck using convolution neural networks. Proceedings of SPIE Medical Imaging 2019: Digital Pathology.<\/p>\n \r\n\r\n\r\n\r\n\r\nHalicek_2019_SPIE\r\n\r\n\t\r\n\r\n<span class=\"pdf\">[<a target=\"_blank\" href=\"https:\/\/www.ncbi.nlm.nih.gov\/pubmed\/32476700\" rel=\"noopener noreferrer\">PubMed<\/a>]<\/span>\r\n\r\n\r\n\t<span class=\"pdf\">[<a target=\"_blank\" href=\"https:\/\/fei-lab.org\/wp-content\/uploads\/2019\/08\/Fei_2019_SPIE_Halicek_Detection_Squamous_Histological_Head_neck_CNN.pdf\" rel=\"noopener noreferrer\">PDF<\/a>]<\/span>\r\n\r\n\r\n\t[<a target=\"_blank\" href=\"https:\/\/doi.org\/10.1117\/12.2512570\" rel=\"noopener noreferrer\">DOI<\/a>]\r\n\r\n\r\n\t[<a download target=\"_blank\" href=\"https:\/\/fei-lab.org\/wp-content\/uploads\/2019\/09\/Halicek_2019_SPIE_BibTeX.txt\" rel=\"noopener noreferrer\">Bibtex<\/a>]\r\n\r\n\r\n\t[<a download target=\"_blank\" href=\"https:\/\/fei-lab.org\/wp-content\/uploads\/2019\/09\/Halicek_2019_SPIE_EndNote.enw\" rel=\"noopener noreferrer\">Endnote<\/a>]\r\n\r\n\r\n\r\n<div style=\"height: 10px; clear: both;\"><\/div>\t\t\r\n\t<\/li>\r\n\r\n<li>\r\n\r\n<p>Yogananda CGB, Nalawade SS, Murugesan GK, Wagner B, Pinho MC, <strong>Fei BW<\/strong>, Madhuranthakam AJ, Maldjian JA. Fully Automated Brain Tumor Segmentation and Survival Prediction of Gliomas using Deep Learning and MRI. bioRxiv (2019):760157.<\/p>\n \r\n\r\n\r\n\r\n\r\nYogananda_2019_bioRxiv\r\n\r\n\t\r\n\r\n<span class=\"pdf\">[<a target=\"_blank\" href=\"https:\/\/www.ncbi.nlm.nih.gov\/pubmed\/32476706\" rel=\"noopener noreferrer\">PubMed<\/a>]<\/span>\r\n\r\n\r\n\t<span class=\"pdf\">[<a target=\"_blank\" href=\"https:\/\/fei-lab.org\/wp-content\/uploads\/2019\/10\/Fei_2019_bioRxiv_Yogananda_Fully_automated_segmentation.pdf\" rel=\"noopener noreferrer\">PDF<\/a>]<\/span>\r\n\r\n\r\n\r\n\t[<a download target=\"_blank\" href=\"https:\/\/fei-lab.org\/wp-content\/uploads\/2019\/10\/Yogananda_2019_bioRxiv_BibTeX.txt\" rel=\"noopener noreferrer\">Bibtex<\/a>]\r\n\r\n\r\n\t[<a download target=\"_blank\" href=\"https:\/\/fei-lab.org\/wp-content\/uploads\/2019\/01\/Yogananda_2019_bioRxiv_EndNote.enw\" rel=\"noopener noreferrer\">Endnote<\/a>]\r\n\r\n\r\n\r\n<div style=\"height: 10px; clear: both;\"><\/div>\t\t\r\n\t<\/li>\r\n\r\n<li>\r\n\r\n<p>Lu G, Wang D, Qin X, Muller S, Little JV, Wang X, Chen AY, Chen G, <strong>Fei BW<\/strong> (Corresponding author). Histopathology feature mining and association with hyperspectral imaging for the detection of squamous neoplasia. Scientific reports; 9(1): 1-13.<\/p>\n \r\n\r\n\r\n\r\n\r\nLu_2019_SR\r\n\r\n\t\r\n\r\n<span class=\"pdf\">[<a target=\"_blank\" href=\"https:\/\/www.ncbi.nlm.nih.gov\/pubmed\/31780698\" rel=\"noopener noreferrer\">PubMed<\/a>]<\/span>\r\n\r\n\r\n\t<span class=\"pdf\">[<a target=\"_blank\" href=\"https:\/\/fei-lab.org\/wp-content\/uploads\/2019\/01\/Fei_2019_SR_Lu_Histopathology_feature_mining.pdf\" rel=\"noopener noreferrer\">PDF<\/a>]<\/span>\r\n\r\n\r\n\t[<a target=\"_blank\" href=\"https:\/\/doi.org\/10.1038\/s41598-019-54139-5\" rel=\"noopener noreferrer\">DOI<\/a>]\r\n\r\n\r\n\t[<a download target=\"_blank\" href=\"https:\/\/fei-lab.org\/wp-content\/uploads\/2020\/01\/Lu_2019_SR_BibTeX.txt\" rel=\"noopener noreferrer\">Bibtex<\/a>]\r\n\r\n\r\n\t[<a download target=\"_blank\" href=\"https:\/\/fei-lab.org\/wp-content\/uploads\/2020\/01\/Lu_2019_SR_EndNote.enw\" rel=\"noopener noreferrer\">Endnote<\/a>]\r\n\r\n\r\n\r\n<div style=\"height: 10px; clear: both;\"><\/div>\t\t\r\n\t<\/li>\r\n\r\n<li>\r\n\r\n<p>Halicek M, Dormer J, Little JV, Chen AY, Myers L, Sumer BD, <strong>Fei BW<\/strong> (Corresponding author). Hyperspectral Imaging of Head and Neck Squamous Cell Carcinoma for Cancer Margin Detection in Surgical Specimens from 102 Patients Using Deep Learning. Cancers (2019);11(9):1367.<\/p>\n \r\n\r\n\r\n\r\n\r\nHalicek_2019_Cancers_1\r\n\r\n\t\r\n\r\n<span class=\"pdf\">[<a target=\"_blank\" href=\"https:\/\/www.ncbi.nlm.nih.gov\/pubmed\/31540063\" rel=\"noopener noreferrer\">PubMed<\/a>]<\/span>\r\n\r\n\r\n\t<span class=\"pdf\">[<a target=\"_blank\" href=\"https:\/\/fei-lab.org\/wp-content\/uploads\/2019\/10\/Fei_2019_Cancers_Halicek_HSI_Head_Neck_Carcinoma_102.pdf\" rel=\"noopener noreferrer\">PDF<\/a>]<\/span>\r\n\r\n\r\n\t[<a target=\"_blank\" href=\"https:\/\/doi.org\/10.3390\/cancers11091367\" rel=\"noopener noreferrer\">DOI<\/a>]\r\n\r\n\r\n\t[<a download target=\"_blank\" href=\"https:\/\/fei-lab.org\/wp-content\/uploads\/2019\/10\/Halicek_2019_Cancers_1_BibTeX.txt\" rel=\"noopener noreferrer\">Bibtex<\/a>]\r\n\r\n\r\n\t[<a download target=\"_blank\" href=\"https:\/\/fei-lab.org\/wp-content\/uploads\/2019\/01\/Halicek_2019_Cancers_1_EndNote.enw\" rel=\"noopener noreferrer\">Endnote<\/a>]\r\n\r\n\r\n\r\n<div style=\"height: 10px; clear: both;\"><\/div>\t\t\r\n\t<\/li>\r\n\r\n<li>\r\n\r\n<p>Halicek M, Fabelo H, Ortega S, Callic\u00f3 GM, <strong>Fei BW<\/strong> (Corresponding author). <em>In-vivo<\/em> and <em>ex-vivo<\/em> tissue analysis through hyperspectral imaging techniques: Revealing the invisible featuers of cancer. Cancers;11(6).<\/p>\n \r\n\r\n\r\n\r\n\r\nHalicek_2019_Cancers\r\n\r\n\t\r\n\r\n<span class=\"pdf\">[<a target=\"_blank\" href=\"https:\/\/www.ncbi.nlm.nih.gov\/pubmed\/31151223\" rel=\"noopener noreferrer\">PubMed<\/a>]<\/span>\r\n\r\n\r\n\t<span class=\"pdf\">[<a target=\"_blank\" href=\"https:\/\/fei-lab.org\/wp-content\/uploads\/2019\/08\/Fei_2019_Cancers_Halicek_Tissue_analysis_Hyperspectral.pdf\" rel=\"noopener noreferrer\">PDF<\/a>]<\/span>\r\n\r\n\r\n\t[<a target=\"_blank\" href=\"https:\/\/doi.org\/10.3390\/cancers11060756\" rel=\"noopener noreferrer\">DOI<\/a>]\r\n\r\n\r\n\t[<a download target=\"_blank\" href=\"https:\/\/fei-lab.org\/wp-content\/uploads\/2019\/09\/Halicek_2019_Cancers_BibTeX.txt\" rel=\"noopener noreferrer\">Bibtex<\/a>]\r\n\r\n\r\n\t[<a download target=\"_blank\" href=\"https:\/\/fei-lab.org\/wp-content\/uploads\/2019\/09\/Halicek_2019_Cancers_EndNote.enw\" rel=\"noopener noreferrer\">Endnote<\/a>]\r\n\r\n\r\n\r\n<div style=\"height: 10px; clear: both;\"><\/div>\t\t\r\n\t<\/li>\r\n\r\n<li>\r\n\r\n<p>Shahedi M, Halicek M, Guo R, Zhang G, Schuster DM, <strong>Fei BW<\/strong> (Corresponding author). A semiautomatic prostate segmentation in CT images using a deep learning approach. The Annual Meeting of the Biomedical Engineering Society.<\/p>\n \r\n\r\n\r\n\r\n\r\n\t\r\n\r\n<span class=\"pdf\">[<a target=\"_blank\" href=\"https:\/\/www.ncbi.nlm.nih.gov\/pubmed\/32528212\" rel=\"noopener noreferrer\">PubMed<\/a>]<\/span>\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n<div style=\"height: 10px; clear: both;\"><\/div>\t\t\r\n\t<\/li>\r\n\r\n<li>\r\n\r\n<p>Tian Z, Liu L, <strong>Fei BW<\/strong>. Deep convolutional neural network for prostate MR segmentation. International Journal of Computer Assisted Radiology and Surgery;13(11):1687.<\/p>\n \r\n\r\n\r\n\r\n\r\nTian_2018_JCARS\r\n\r\n\t\r\n\r\n\r\n\t<span class=\"pdf\">[<a target=\"_blank\" href=\"https:\/\/fei-lab.org\/wp-content\/uploads\/2019\/08\/Fei_2018_JCARS_Tian_Deep_CNN_Prostate_segmentation.pdf\" rel=\"noopener noreferrer\">PDF<\/a>]<\/span>\r\n\r\n\r\n\r\n\t[<a download target=\"_blank\" href=\"https:\/\/fei-lab.org\/wp-content\/uploads\/2019\/09\/Tian_2018_JCARS_BibTeX.txt\" rel=\"noopener noreferrer\">Bibtex<\/a>]\r\n\r\n\r\n\t[<a download target=\"_blank\" href=\"https:\/\/fei-lab.org\/wp-content\/uploads\/2019\/09\/Tian_2018_JCARS_EndNote.enw\" rel=\"noopener noreferrer\">Endnote<\/a>]\r\n\r\n\r\n\r\n<div style=\"height: 10px; clear: both;\"><\/div>\t\t\r\n\t<\/li>\r\n\r\n<li>\r\n\r\n<p>Lu G, Wang DS, Qin X, Muller S, Wang X, Chen AY, Chen ZG, <strong>Fei BW<\/strong> (Corresponding author). Detection and delineation of squamous neoplasia with hyperspectral imaging in a mouse model of tongue carcinogenesis. Journal of Biophotonics;11(3):e201700078.<\/p>\n \r\n\r\n\r\n\r\n\r\nLu_2018_JBiophotonics\r\n\r\n\t\r\n\r\n<span class=\"pdf\">[<a target=\"_blank\" href=\"https:\/\/www.ncbi.nlm.nih.gov\/pubmed\/28921845\" rel=\"noopener noreferrer\">PubMed<\/a>]<\/span>\r\n\r\n\r\n\t<span class=\"pdf\">[<a target=\"_blank\" href=\"https:\/\/fei-lab.org\/wp-content\/uploads\/2019\/08\/Fei_2018_JBiophotonics_Lu_Detection_Delineation_Neoplasia_Hyperspectral_Mouse_Tongue.pdf\" rel=\"noopener noreferrer\">PDF<\/a>]<\/span>\r\n\r\n\r\n\t[<a target=\"_blank\" href=\"https:\/\/doi.org\/10.1002\/jbio.201700078\" rel=\"noopener noreferrer\">DOI<\/a>]\r\n\r\n\r\n\t[<a download target=\"_blank\" href=\"https:\/\/fei-lab.org\/wp-content\/uploads\/2019\/09\/Lu_2018_JBiophotonics_BibTeX.txt\" rel=\"noopener noreferrer\">Bibtex<\/a>]\r\n\r\n\r\n\t[<a download target=\"_blank\" href=\"https:\/\/fei-lab.org\/wp-content\/uploads\/2019\/09\/Lu_2018_JBiophotonics_EndNote.enw\" rel=\"noopener noreferrer\">Endnote<\/a>]\r\n\r\n\r\n\r\n<div style=\"height: 10px; clear: both;\"><\/div>\t\t\r\n\t<\/li>\r\n\r\n<li>\r\n\r\n<p>Tian Z, Liu L, Zhang Z, <strong>Fei BW<\/strong> (Corresponding author). PSNet: prostate segmentation on MRI based on a convolutional neural network. Journal of Medical Imaging;5(2):021208.<\/p>\n \r\n\r\n\r\n\r\n\r\nTian_2018_JMI\r\n\r\n\t\r\n\r\n<span class=\"pdf\">[<a target=\"_blank\" href=\"https:\/\/www.ncbi.nlm.nih.gov\/pubmed\/29376105\" rel=\"noopener noreferrer\">PubMed<\/a>]<\/span>\r\n\r\n\r\n\t<span class=\"pdf\">[<a target=\"_blank\" href=\"https:\/\/fei-lab.org\/wp-content\/uploads\/2019\/08\/Fei_2018_JMI_Tian_PSNet_Prostate_segmentation_CNN.pdf\" rel=\"noopener noreferrer\">PDF<\/a>]<\/span>\r\n\r\n\r\n\t[<a target=\"_blank\" href=\"https:\/\/doi.org\/10.1117\/1.JMI.5.2.021208\" rel=\"noopener noreferrer\">DOI<\/a>]\r\n\r\n\r\n\t[<a download target=\"_blank\" href=\"https:\/\/fei-lab.org\/wp-content\/uploads\/2019\/09\/Tian_2018_JMI_BibTeX.txt\" rel=\"noopener noreferrer\">Bibtex<\/a>]\r\n\r\n\r\n\t[<a download target=\"_blank\" href=\"https:\/\/fei-lab.org\/wp-content\/uploads\/2019\/09\/Tian_2018_JMI_EndNote.enw\" rel=\"noopener noreferrer\">Endnote<\/a>]\r\n\r\n\r\n\r\n<div style=\"height: 10px; clear: both;\"><\/div>\t\t\r\n\t<\/li>\r\n\r\n<li>\r\n\r\n<p>Sarno, A., Dance, DR., van Engen, RE., Young, KC., Russo, P., Di Lillo, F., Mettivier, G., Bliznakova, K., <strong>Fei, BW<\/strong>., Sechopoulos, I.(2017). \u201cA Monte Carlo model for mean glandular dose evaluation in spot compression mammography.\u201dMedical Physics 44(7): 3848-3860.<\/p>\n \r\n\r\n\r\n\r\n\r\nSarno_2017_MedPhys\r\n\r\n\t\r\n\r\n<span class=\"pdf\">[<a target=\"_blank\" href=\"https:\/\/www.ncbi.nlm.nih.gov\/pubmed\/28500759\" rel=\"noopener noreferrer\">PubMed<\/a>]<\/span>\r\n\r\n\r\n\t<span class=\"pdf\">[<a target=\"_blank\" href=\"https:\/\/fei-lab.org\/wp-content\/uploads\/2019\/08\/Fei_2017_MedPhys_Sarno_MedPhys_Monte_Carlo_Evaluation_Mammography.pdf\" rel=\"noopener noreferrer\">PDF<\/a>]<\/span>\r\n\r\n\r\n\t[<a target=\"_blank\" href=\"https:\/\/doi.org\/10.1002\/mp.12339\" rel=\"noopener noreferrer\">DOI<\/a>]\r\n\r\n\r\n\t[<a download target=\"_blank\" href=\"https:\/\/fei-lab.org\/wp-content\/uploads\/2017\/01\/Sarno_2017_MedPhys_BibTeX.txt\" rel=\"noopener noreferrer\">Bibtex<\/a>]\r\n\r\n\r\n\t[<a download target=\"_blank\" href=\"https:\/\/fei-lab.org\/wp-content\/uploads\/2019\/09\/Sarno_2017_MedPhys_EndNote.enw\" rel=\"noopener noreferrer\">Endnote<\/a>]\r\n\r\n\r\n\r\n<div style=\"height: 10px; clear: both;\"><\/div>\t\t\r\n\t<\/li>\r\n\r\n<li>\r\n\r\n<p>Ma L, Guo R, Zhang G, Tade F, Schuster DM, <strong>Fei BW<\/strong>, Automatic segmentation of the prostate on CT images using deep learning and multi-atlas fusion, Proc. SPIE 10133, Medical Imaging 2017: Image Processing, 101332O, February 24, 2017, Orlando, FL.<\/p>\n \r\n\r\n\r\n\r\n\r\nMa_2017_MedImaging_1\r\n\r\n\t\r\n\r\n<span class=\"pdf\">[<a target=\"_blank\" href=\"https:\/\/www.ncbi.nlm.nih.gov\/pubmed\/30220767\" rel=\"noopener noreferrer\">PubMed<\/a>]<\/span>\r\n\r\n\r\n\t<span class=\"pdf\">[<a target=\"_blank\" href=\"https:\/\/fei-lab.org\/wp-content\/uploads\/2019\/08\/Fei_2017_SPIE_Ma_Automatic_segmentation_Prostate_Deep_learning_Multi_atlas.pdf\" rel=\"noopener noreferrer\">PDF<\/a>]<\/span>\r\n\r\n\r\n\t[<a target=\"_blank\" href=\"https:\/\/doi.org\/10.1117\/12.2255755\" rel=\"noopener noreferrer\">DOI<\/a>]\r\n\r\n\r\n\t[<a download target=\"_blank\" href=\"https:\/\/fei-lab.org\/wp-content\/uploads\/2019\/09\/Ma_2017_MedImaging_1_BibTeX.txt\" rel=\"noopener noreferrer\">Bibtex<\/a>]\r\n\r\n\r\n\t[<a download target=\"_blank\" href=\"https:\/\/fei-lab.org\/wp-content\/uploads\/2019\/09\/Ma_2017_MedImaging_1_EndNote.enw\" rel=\"noopener noreferrer\">Endnote<\/a>]\r\n\r\n\r\n\r\n<div style=\"height: 10px; clear: both;\"><\/div>\t\t\r\n\t<\/li>\r\n\r\n<li>\r\n\r\n<p><strong>Fei BW<\/strong>, Lu G, Wang X, Zhang HZ, Little JV, Patel MR, Griffith CC, El-Diery MW, Chen AY. Label-free reflectance hyperspectral imaging for tumor margin assessment: a pilot study on surgical specimens of cancer patients. Journal of Biomedical Optics;22(8):086009.<\/p>\n \r\n\r\n\r\n\r\n\r\nFei_2017_JBO\r\n\r\n\t\r\n\r\n<span class=\"pdf\">[<a target=\"_blank\" href=\"https:\/\/www.ncbi.nlm.nih.gov\/pubmed\/28849631\" rel=\"noopener noreferrer\">PubMed<\/a>]<\/span>\r\n\r\n\r\n\t<span class=\"pdf\">[<a target=\"_blank\" href=\"https:\/\/fei-lab.org\/wp-content\/uploads\/2019\/10\/Fei_2017_Label_free_reflectance_HSI.pdf\" rel=\"noopener noreferrer\">PDF<\/a>]<\/span>\r\n\r\n\r\n\t[<a target=\"_blank\" href=\"https:\/\/doi.org\/10.1117\/1.JBO.22.8.086009\" rel=\"noopener noreferrer\">DOI<\/a>]\r\n\r\n\r\n\t[<a download target=\"_blank\" href=\"https:\/\/fei-lab.org\/wp-content\/uploads\/2019\/10\/Fei_2017_JBO_BibTeX.txt\" rel=\"noopener noreferrer\">Bibtex<\/a>]\r\n\r\n\r\n\t[<a download target=\"_blank\" href=\"https:\/\/fei-lab.org\/wp-content\/uploads\/2019\/10\/Fei_2017_JBO_EndNote.enw\" rel=\"noopener noreferrer\">Endnote<\/a>]\r\n\r\n\r\n\r\n<div style=\"height: 10px; clear: both;\"><\/div>\t\t\r\n\t<\/li>\r\n\r\n<li>\r\n\r\n<p>Chung H, Lu G, Tian Z, Wang D, Chen ZG, <strong>Fei BW<\/strong> (Corresponding author). Superpixel-based spectral classification for the detection of head and neck cancer with hyperspectral imaging. Proceedings of SPIE \u2013 The International Society for Optical Engineering 2016; 9788:978813. PubMed PMID:27656035.<\/p>\n \r\n\r\n\r\n\r\n\r\nChung_2016_MedImaging\r\n\r\n\t\r\n\r\n<span class=\"pdf\">[<a target=\"_blank\" href=\"https:\/\/www.ncbi.nlm.nih.gov\/pubmed\/27656035\" rel=\"noopener noreferrer\">PubMed<\/a>]<\/span>\r\n\r\n\r\n\t<span class=\"pdf\">[<a target=\"_blank\" href=\"https:\/\/fei-lab.org\/wp-content\/uploads\/2019\/08\/Fei_2016_SPIE_Chung_Superpixel_Spectral_Classification_Head_Neck_HSI.pdf\" rel=\"noopener noreferrer\">PDF<\/a>]<\/span>\r\n\r\n\r\n\r\n\t[<a download target=\"_blank\" href=\"https:\/\/fei-lab.org\/wp-content\/uploads\/2019\/09\/Chung_2016_MedImaging_BibTeX.txt\" rel=\"noopener noreferrer\">Bibtex<\/a>]\r\n\r\n\r\n\t[<a download target=\"_blank\" href=\"https:\/\/fei-lab.org\/wp-content\/uploads\/2019\/09\/Chung_2016_MedImaging_EndNote.enw\" rel=\"noopener noreferrer\">Endnote<\/a>]\r\n\r\n\r\n\r\n<div style=\"height: 10px; clear: both;\"><\/div>\t\t\r\n\t<\/li>\r\n\r\n<li>\r\n\r\n<p>Pike R, Lu G, Wang D, Chen ZG, <strong>Fei BW<\/strong> (Corresponding author). A minimum spanning forest based classification method for dedicated breast CT images. Medical Physics 2015; 42:6190-202.<\/p>\n \r\n\r\n\r\n\r\n\r\nPike_2015_IEEE\r\n\r\n\t\r\n\r\n<span class=\"pdf\">[<a target=\"_blank\" href=\"https:\/\/www.ncbi.nlm.nih.gov\/pubmed\/26520712\" rel=\"noopener noreferrer\">PubMed<\/a>]<\/span>\r\n\r\n\r\n\t<span class=\"pdf\">[<a target=\"_blank\" href=\"https:\/\/fei-lab.org\/wp-content\/uploads\/2015\/01\/Fei_2015_MedPhys_Pike_Minimum_spanning_forest_CT_Breast_Cancer.pdf\" rel=\"noopener noreferrer\">PDF<\/a>]<\/span>\r\n\r\n\r\n\t[<a target=\"_blank\" href=\"https:\/\/doi.org\/10.1118\/1.4931958\" rel=\"noopener noreferrer\">DOI<\/a>]\r\n\r\n\r\n\t[<a download target=\"_blank\" href=\"https:\/\/fei-lab.org\/wp-content\/uploads\/2019\/09\/Pike_2015_IEEE_BibTeX.txt\" rel=\"noopener noreferrer\">Bibtex<\/a>]\r\n\r\n\r\n\t[<a download target=\"_blank\" href=\"https:\/\/fei-lab.org\/wp-content\/uploads\/2019\/09\/Pike_2015_IEEE_EndNote.enw\" rel=\"noopener noreferrer\">Endnote<\/a>]\r\n\r\n\r\n\r\n<div style=\"height: 10px; clear: both;\"><\/div>\t\t\r\n\t<\/li>\r\n\r\n<li>\r\n\r\n<p>Pike R, Sechopoulos I, <strong>Fei BW<\/strong> (Corresponding author). A minimum spanning forest-based method for noninvasive cancer detection wtih hyperspectral imaging. IEEE Transactions on Biomedical Engineering 2015; 63(3):653-663.<\/p>\n \r\n\r\n\r\n\r\n\r\nPike_2015_MedPhys\r\n\r\n\t\r\n\r\n<span class=\"pdf\">[<a target=\"_blank\" href=\"https:\/\/www.ncbi.nlm.nih.gov\/pubmed\/26285052\" rel=\"noopener noreferrer\">PubMed<\/a>]<\/span>\r\n\r\n\r\n\t<span class=\"pdf\">[<a target=\"_blank\" href=\"https:\/\/fei-lab.org\/wp-content\/uploads\/2015\/01\/Fei_2015_IEEE_Pike_Minimum_spanning_forest_Noninvasive_Detection_HSI.pdf\" rel=\"noopener noreferrer\">PDF<\/a>]<\/span>\r\n\r\n\r\n\t[<a target=\"_blank\" href=\"https:\/\/doi.org\/10.1109\/TBME.2015.2468578\" rel=\"noopener noreferrer\">DOI<\/a>]\r\n\r\n\r\n\t[<a download target=\"_blank\" href=\"https:\/\/fei-lab.org\/wp-content\/uploads\/2019\/09\/Pike_2015_MedPhys_BibTeX.txt\" rel=\"noopener noreferrer\">Bibtex<\/a>]\r\n\r\n\r\n\t[<a download target=\"_blank\" href=\"https:\/\/fei-lab.org\/wp-content\/uploads\/2019\/09\/Pike_2015_MedPhys_EndNote.enw\" rel=\"noopener noreferrer\">Endnote<\/a>]\r\n\r\n\r\n\r\n<div style=\"height: 10px; clear: both;\"><\/div>\t\t\r\n\t<\/li>\r\n\r\n<li>\r\n\r\n<p>Qin X, Wang S, Shen M, Zhang X, Lerakis S, Wagner MB, <strong>Fei BW<\/strong> (Corresponding author). Register cardiac fiber orientations from 3D DTI volume to 2D ultrasound image of rat hearts. Proceedings of SPIE \u2013 The International Society for Optical Engineering 2015; 9415:94152M. PubMed PMID:26855466.<\/p>\n \r\n\r\n\r\n\r\n\r\nQin_2015_MedImaging\r\n\r\n\t\r\n\r\n<span class=\"pdf\">[<a target=\"_blank\" href=\"https:\/\/www.ncbi.nlm.nih.gov\/pubmed\/26855466\" rel=\"noopener noreferrer\">PubMed<\/a>]<\/span>\r\n\r\n\r\n\t<span class=\"pdf\">[<a target=\"_blank\" href=\"https:\/\/fei-lab.org\/wp-content\/uploads\/2019\/08\/Fei_2015_SPIE_Qin_Register_cardiac_fiber_DTI_Ultrasound_Rat_hearts.pdf\" rel=\"noopener noreferrer\">PDF<\/a>]<\/span>\r\n\r\n\r\n\r\n\t[<a download target=\"_blank\" href=\"https:\/\/fei-lab.org\/wp-content\/uploads\/2019\/09\/Qin_2015_MedImaging_BibTeX.txt\" rel=\"noopener noreferrer\">Bibtex<\/a>]\r\n\r\n\r\n\t[<a download target=\"_blank\" href=\"https:\/\/fei-lab.org\/wp-content\/uploads\/2019\/09\/Qin_2015_MedImaging_EndNote.enw\" rel=\"noopener noreferrer\">Endnote<\/a>]\r\n\r\n\r\n\r\n<div style=\"height: 10px; clear: both;\"><\/div>\t\t\r\n\t<\/li>\r\n\r\n<li>\r\n\r\n<p>Lu G, Halig L, Wang D, Chen ZG, <strong>Fei BW<\/strong> (Corresponding author). Spectral-spatial classification using tensor modeling for cancer detection with hyperspectral imaging. Proceedings of SPIE \u2013 The International Society for Optical Engineering 2014; 9034:903413. PubMed PMID:25328639.<\/p>\n \r\n\r\n\r\n\r\n\r\nLu_2014_MedImaging\r\n\r\n\t\r\n\r\n<span class=\"pdf\">[<a target=\"_blank\" href=\"https:\/\/www.ncbi.nlm.nih.gov\/pubmed\/25328639\" rel=\"noopener noreferrer\">PubMed<\/a>]<\/span>\r\n\r\n\r\n\t<span class=\"pdf\">[<a target=\"_blank\" href=\"https:\/\/fei-lab.org\/wp-content\/uploads\/2019\/08\/Fei_2014_SPIE_Lu_Spectral_Spatial_Tensor_modeling_HSI.pdf\" rel=\"noopener noreferrer\">PDF<\/a>]<\/span>\r\n\r\n\r\n\r\n\t[<a download target=\"_blank\" href=\"https:\/\/fei-lab.org\/wp-content\/uploads\/2014\/01\/Lu_2014_MedImaging_BibTeX.txt\" rel=\"noopener noreferrer\">Bibtex<\/a>]\r\n\r\n\r\n\t[<a download target=\"_blank\" href=\"https:\/\/fei-lab.org\/wp-content\/uploads\/2019\/09\/Lu_2014_MedImaging_EndNote.enw\" rel=\"noopener noreferrer\">Endnote<\/a>]\r\n\r\n\r\n\r\n<div style=\"height: 10px; clear: both;\"><\/div>\t\t\r\n\t<\/li>\r\n\r\n<li>\r\n\r\n<p>Akbari H, <strong>Fei BW<\/strong> (Corresponding author). Automatic 3D segmentation of the kidney in MR images using wavelet feature extraction and probability shape model. Proceedings of SPIE \u2013 The International Society for Optical Engineering 2013; 8314:83143D. PubMed PMID:24027620.<\/p>\n \r\n\r\n\r\n\r\n\r\nAkbari_2013_MedImaging\r\n\r\n\t\r\n\r\n<span class=\"pdf\">[<a target=\"_blank\" href=\"https:\/\/www.ncbi.nlm.nih.gov\/pubmed\/24027620\" rel=\"noopener noreferrer\">PubMed<\/a>]<\/span>\r\n\r\n\r\n\t<span class=\"pdf\">[<a target=\"_blank\" href=\"https:\/\/fei-lab.org\/wp-content\/uploads\/2019\/08\/Fei_2013_Akbari_Automatic_3DSegment_Kidney.pdf\" rel=\"noopener noreferrer\">PDF<\/a>]<\/span>\r\n\r\n\r\n\r\n\t[<a download target=\"_blank\" href=\"https:\/\/fei-lab.org\/wp-content\/uploads\/2013\/01\/Akbari_2013_MedImaging_BibTeX.txt\" rel=\"noopener noreferrer\">Bibtex<\/a>]\r\n\r\n\r\n\t[<a download target=\"_blank\" href=\"https:\/\/fei-lab.org\/wp-content\/uploads\/2013\/01\/Akbari_2013_MedImaging_EndNote.enw\" rel=\"noopener noreferrer\">Endnote<\/a>]\r\n\r\n\r\n\r\n<div style=\"height: 10px; clear: both;\"><\/div>\t\t\r\n\t<\/li>\r\n\r\n<li>\r\n\r\n<p>Halig LV, Wang D, Wang AY, Chen ZG, <strong>Fei BW<\/strong> (Corresponding author).Biodistribution study of nanoparticle encapsulated photodynamic therapy drugs using multispectral imaging. Proceedings of SPIE \u2013 The International Society for Optical Engineering 2013; 8672. PubMed PMID:24236230.<\/p>\n \r\n\r\n\r\n\r\n\r\nHalig_2013_MedImaging\r\n\r\n\t\r\n\r\n<span class=\"pdf\">[<a target=\"_blank\" href=\"https:\/\/www.ncbi.nlm.nih.gov\/pubmed\/24236230\" rel=\"noopener noreferrer\">PubMed<\/a>]<\/span>\r\n\r\n\r\n\t<span class=\"pdf\">[<a target=\"_blank\" href=\"https:\/\/fei-lab.org\/wp-content\/uploads\/2019\/08\/Fei_2013_Halig_Biodistribution_Nanoparticle.pdf\" rel=\"noopener noreferrer\">PDF<\/a>]<\/span>\r\n\r\n\r\n\t[<a target=\"_blank\" href=\"https:\/\/doi.org\/10.1117\/12.2006492\" rel=\"noopener noreferrer\">DOI<\/a>]\r\n\r\n\r\n\t[<a download target=\"_blank\" href=\"https:\/\/fei-lab.org\/wp-content\/uploads\/2019\/09\/Halig_2013_MedImaging_BibTeX.txt\" rel=\"noopener noreferrer\">Bibtex<\/a>]\r\n\r\n\r\n\t[<a download target=\"_blank\" href=\"https:\/\/fei-lab.org\/wp-content\/uploads\/2019\/09\/Halig_2013_MedImaging_EndNote.enw\" rel=\"noopener noreferrer\">Endnote<\/a>]\r\n\r\n\r\n\r\n<div style=\"height: 10px; clear: both;\"><\/div>\t\t\r\n\t<\/li>\r\n\r\n<li>\r\n\r\n<p>Yang X, <strong>Fei BW<\/strong> (Corresponding author).3D prostate segmentation of ultrasound images combining longitudinal image registration and machine learning. Proceedings of SPIE \u2013 The International Society for Optical Engineering 2012; 8316:83162O. PubMed PMID:24027622.<\/p>\n \r\n\r\n\r\n\r\n\r\nYang_2012_MedImaging\r\n\r\n\t\r\n\r\n<span class=\"pdf\">[<a target=\"_blank\" href=\"https:\/\/www.ncbi.nlm.nih.gov\/pubmed\/24027622\" rel=\"noopener noreferrer\">PubMed<\/a>]<\/span>\r\n\r\n\r\n\t<span class=\"pdf\">[<a target=\"_blank\" href=\"https:\/\/fei-lab.org\/wp-content\/uploads\/2019\/08\/Fei_2012_Yang_SPIE_Prostate_Segmentation.pdf\" rel=\"noopener noreferrer\">PDF<\/a>]<\/span>\r\n\r\n\r\n\r\n\t[<a download target=\"_blank\" href=\"https:\/\/fei-lab.org\/wp-content\/uploads\/2019\/09\/Yang_2012_MedImaging_BibTeX.txt\" rel=\"noopener noreferrer\">Bibtex<\/a>]\r\n\r\n\r\n\t[<a download target=\"_blank\" href=\"https:\/\/fei-lab.org\/wp-content\/uploads\/2019\/09\/Yang_2012_MedImaging_EndNote.enw\" rel=\"noopener noreferrer\">Endnote<\/a>]\r\n\r\n\r\n\r\n<div style=\"height: 10px; clear: both;\"><\/div>\t\t\r\n\t<\/li>\r\n\r\n<li>\r\n\r\n<p>Wang H, <strong>Fei BW<\/strong> (Corresponding author). An MR image-guided, voxel-based partial volume correction method for PET images. Medical Physics 2012; 39:179-195.<\/p>\n \r\n\r\n\r\n\r\n\r\nWang_2012_MedPhys\r\n\r\n\t\r\n\r\n<span class=\"pdf\">[<a target=\"_blank\" href=\"https:\/\/www.ncbi.nlm.nih.gov\/pubmed\/22225287\" rel=\"noopener noreferrer\">PubMed<\/a>]<\/span>\r\n\r\n\r\n\t<span class=\"pdf\">[<a target=\"_blank\" href=\"https:\/\/fei-lab.org\/wp-content\/uploads\/2017\/03\/Fei_2012_Wang_Med_Phys_PVC.pdf\" rel=\"noopener noreferrer\">PDF<\/a>]<\/span>\r\n\r\n\r\n\t[<a target=\"_blank\" href=\"https:\/\/doi.org\/10.1118\/1.3665704\" rel=\"noopener noreferrer\">DOI<\/a>]\r\n\r\n\r\n\t[<a download target=\"_blank\" href=\"https:\/\/fei-lab.org\/wp-content\/uploads\/2019\/09\/Wang_2012_MedPhys_BibTeX.txt\" rel=\"noopener noreferrer\">Bibtex<\/a>]\r\n\r\n\r\n\t[<a download target=\"_blank\" href=\"https:\/\/fei-lab.org\/wp-content\/uploads\/2019\/09\/Wang_2012_MedPhys_EndNote.enw\" rel=\"noopener noreferrer\">Endnote<\/a>]\r\n\r\n\r\n\r\n<div style=\"height: 10px; clear: both;\"><\/div>\t\t\r\n\t<\/li>\r\n\r\n<li>\r\n\r\n<p>Akbari H, Halig LV, Schuster DM, Osunkoya A, Master VA, Nieh PT, Chen GZ, and <strong>Fei BW<\/strong> (Corresponding author). Hyperspectral imaging and quantitative analysis for prostate cancer detection, Journal of Biomedical Optics 2012; 17:076005.<\/p>\n \r\n\r\n\r\n\r\n\r\nAkbari_2012_JBO\r\n\r\n\t\r\n\r\n<span class=\"pdf\">[<a target=\"_blank\" href=\"https:\/\/www.ncbi.nlm.nih.gov\/pubmed\/22894488\" rel=\"noopener noreferrer\">PubMed<\/a>]<\/span>\r\n\r\n\r\n\t<span class=\"pdf\">[<a target=\"_blank\" href=\"https:\/\/fei-lab.org\/wp-content\/uploads\/2017\/03\/Fei_2012_JBO_Hyperspectral_Imaging1.pdf\" rel=\"noopener noreferrer\">PDF<\/a>]<\/span>\r\n\r\n\r\n\t[<a target=\"_blank\" href=\"https:\/\/doi.org\/10.1117\/1.JBO.17.7.076005\" rel=\"noopener noreferrer\">DOI<\/a>]\r\n\r\n\r\n\t[<a download target=\"_blank\" href=\"https:\/\/fei-lab.org\/wp-content\/uploads\/2019\/09\/Akbari_2012_JBO_BibTeX.txt\" rel=\"noopener noreferrer\">Bibtex<\/a>]\r\n\r\n\r\n\t[<a download target=\"_blank\" href=\"https:\/\/fei-lab.org\/wp-content\/uploads\/2019\/09\/Akbari_2012_JBO_EndNote.enw\" rel=\"noopener noreferrer\">Endnote<\/a>]\r\n\r\n\r\n\r\n<div style=\"height: 10px; clear: both;\"><\/div>\t\t\r\n\t<\/li>\r\n\r\n<li>\r\n\r\n<p>Yang X, Akbari H, Halig L, <strong>Fei BW<\/strong> (Corresponding author). 3D non-rigid registration using surface and local salient features for transrectal ultrasound image-guided prostate biopsy. Proceedings of SPIE \u2013 The International Society for Optical Engineering 2011; 7964:79642V. PubMed PMID:24027609.<\/p>\n \r\n\r\n\r\n\r\n\r\nYang_2011_MedImaging_2\r\n\r\n\t\r\n\r\n<span class=\"pdf\">[<a target=\"_blank\" href=\"https:\/\/www.ncbi.nlm.nih.gov\/pubmed\/24027609\" rel=\"noopener noreferrer\">PubMed<\/a>]<\/span>\r\n\r\n\r\n\t<span class=\"pdf\">[<a target=\"_blank\" href=\"https:\/\/fei-lab.org\/wp-content\/uploads\/2011\/01\/Fei_2013_DI_Europe_Biopsy_New2.pdf\" rel=\"noopener noreferrer\">PDF<\/a>]<\/span>\r\n\r\n\r\n\r\n\t[<a download target=\"_blank\" href=\"https:\/\/fei-lab.org\/wp-content\/uploads\/2019\/09\/Yang_2011_MedImaging_2_BibTeX.txt\" rel=\"noopener noreferrer\">Bibtex<\/a>]\r\n\r\n\r\n\t[<a download target=\"_blank\" href=\"https:\/\/fei-lab.org\/wp-content\/uploads\/2019\/09\/Yang_2011_MedImaging_2_EndNote.enw\" rel=\"noopener noreferrer\">Endnote<\/a>]\r\n\r\n\r\n\r\n<div style=\"height: 10px; clear: both;\"><\/div>\t\t\r\n\t<\/li>\r\n\r\n<li>\r\n\r\n<p>Yang X, Schuster D, Master V, Nieh P, Fenster A, <strong>Fei BW<\/strong> (Corresponding author). Automatic 3D segmentation of ultrasound images using atlas registration and statistical texture prior. Proceedings of SPIE \u2013 The International Society for Optical Engineering 2011; 7964:796432. PubMed PMID:22708024. (Cum Laude Poster Award &#8211; First Place).<\/p>\n \r\n\r\n\r\n\r\n\r\nYang_2011_MedImaging_1\r\n\r\n\t\r\n\r\n<span class=\"pdf\">[<a target=\"_blank\" href=\"https:\/\/www.ncbi.nlm.nih.gov\/pubmed\/22708024\" rel=\"noopener noreferrer\">PubMed<\/a>]<\/span>\r\n\r\n\r\n\t<span class=\"pdf\">[<a target=\"_blank\" href=\"https:\/\/fei-lab.org\/wp-content\/uploads\/2011\/01\/Fei_2011_SPIE_Ultrasound_Segmentation-1.pdf\" rel=\"noopener noreferrer\">PDF<\/a>]<\/span>\r\n\r\n\r\n\r\n\t[<a download target=\"_blank\" href=\"https:\/\/fei-lab.org\/wp-content\/uploads\/2019\/09\/Yang_2011_MedImaging_1_BibTeX.txt\" rel=\"noopener noreferrer\">Bibtex<\/a>]\r\n\r\n\r\n\t[<a download target=\"_blank\" href=\"https:\/\/fei-lab.org\/wp-content\/uploads\/2019\/09\/Yang_2011_MedImaging_1_EndNote.enw\" rel=\"noopener noreferrer\">Endnote<\/a>]\r\n\r\n\r\n\r\n<div style=\"height: 10px; clear: both;\"><\/div>\t\t\r\n\t<\/li>\r\n\r\n<li>\r\n\r\n<p><strong>Fei BW<\/strong>, Yang X, Nye JA, Aarsvold JN, Meltzer CC, Votaw JR. PET-MRI quantification tools \u2013 registration, segmentation, classification, and attenuation correction. IEEE Nuclear Science Symposium and Medical Imaging Conference Focused Workshop on PET\/MRI, November 1, 2010, Knoxville, TN.<\/p>\n \r\n\r\n\r\n\r\n\r\n\t\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n<div style=\"height: 10px; clear: both;\"><\/div>\t\t\r\n\t<\/li>\r\n\r\n<li>\r\n\r\n<p>Wang H, Feyes D, Mulvihill J, Oleinick N, Maclennan G, <strong>Fei BW<\/strong> (Corresponding author). Multiscale fuzzy C-means image classification for multiple weighted MR images for the assessment of photodynamic therapy in mice. Proceedings of SPIE \u2013 The International Society for Optical Engineering 2007; 6512. PubMed PMID:24386526.<\/p>\n \r\n\r\n\r\n\r\n\r\nWang_2007_MedImaging\r\n\r\n\t\r\n\r\n<span class=\"pdf\">[<a target=\"_blank\" href=\"https:\/\/www.ncbi.nlm.nih.gov\/pubmed\/24386526\" rel=\"noopener noreferrer\">PubMed<\/a>]<\/span>\r\n\r\n\r\n\t<span class=\"pdf\">[<a target=\"_blank\" href=\"https:\/\/fei-lab.org\/wp-content\/uploads\/2007\/01\/Fei_2007_SPIE_Hesheng-Fuzzy_Classification_Full.pdf\" rel=\"noopener noreferrer\">PDF<\/a>]<\/span>\r\n\r\n\r\n\r\n\t[<a download target=\"_blank\" href=\"https:\/\/fei-lab.org\/wp-content\/uploads\/2019\/09\/Wang_2007_MedImaging_BibTeX.txt\" rel=\"noopener noreferrer\">Bibtex<\/a>]\r\n\r\n\r\n\t[<a download target=\"_blank\" href=\"https:\/\/fei-lab.org\/wp-content\/uploads\/2019\/09\/Wang_2007_MedImaging_EndNote.enw\" rel=\"noopener noreferrer\">Endnote<\/a>]\r\n\r\n\r\n\r\n<div style=\"height: 10px; clear: both;\"><\/div>\t\t\r\n\t<\/li>\r\n\r\n<li>\r\n\r\n<p>Haaga JR, Exner A, <strong>Fei BW<\/strong>, Seftel A. Semiquantitative imaging measurement of baseline and vasomodulated normal prostatic blood flow using sildenafil. International Journal of Impotence Research 2007; 19:110-3.<\/p>\n \r\n\r\n\r\n\r\n\r\nHaaga_2007_IJIR\r\n\r\n\t\r\n\r\n\r\n\t<span class=\"pdf\">[<a target=\"_blank\" href=\"https:\/\/fei-lab.org\/wp-content\/uploads\/2017\/03\/Fei_2007_Haaga_DCE_MRI_prostate_blood_flow.pdf\" rel=\"noopener noreferrer\">PDF<\/a>]<\/span>\r\n\r\n\r\n\r\n\t[<a download target=\"_blank\" href=\"https:\/\/fei-lab.org\/wp-content\/uploads\/2019\/09\/Haaga_2007_IJIR_BibTeX.txt\" rel=\"noopener noreferrer\">Bibtex<\/a>]\r\n\r\n\r\n\t[<a download target=\"_blank\" href=\"https:\/\/fei-lab.org\/wp-content\/uploads\/2019\/09\/Haaga_2007_IJIR_EndNote.enw\" rel=\"noopener noreferrer\">Endnote<\/a>]\r\n\r\n\r\n\r\n<div style=\"height: 10px; clear: both;\"><\/div>\t\t\r\n\t<\/li>\r\n\r\n<li>\r\n\r\n<p><strong>Fei BW<\/strong> (Corresponding author), Chen X, Wang H, Sabol JM, DuPont E, Gilkeson RC. Automatic registration of CT volumes and dual-energy digital radiography for detection of cardiac and lung diseases. Proceedings of IEEE Engineering in Medicine and Biology Society 2006; 1:1976-9. PubMed PMID:17945687.<\/p>\n \r\n\r\n\r\n\r\n\r\nFei_2006_IEEE\r\n\r\n\t\r\n\r\n<span class=\"pdf\">[<a target=\"_blank\" href=\"https:\/\/www.ncbi.nlm.nih.gov\/pubmed\/24386527\" rel=\"noopener noreferrer\">PubMed<\/a>]<\/span>\r\n\r\n\r\n\t<span class=\"pdf\">[<a target=\"_blank\" href=\"https:\/\/fei-lab.org\/wp-content\/uploads\/2019\/09\/Fei_2006_IEEE_CT_Dual-energy_Cardiac.pdf\" rel=\"noopener noreferrer\">PDF<\/a>]<\/span>\r\n\r\n\r\n\r\n\t[<a download target=\"_blank\" href=\"https:\/\/fei-lab.org\/wp-content\/uploads\/2019\/09\/Fei_2006_IEEE_BibTeX.txt\" rel=\"noopener noreferrer\">Bibtex<\/a>]\r\n\r\n\r\n\t[<a download target=\"_blank\" href=\"https:\/\/fei-lab.org\/wp-content\/uploads\/2019\/09\/Fei_2006_IEEE_EndNote.enw\" rel=\"noopener noreferrer\">Endnote<\/a>]\r\n\r\n\r\n\r\n<div style=\"height: 10px; clear: both;\"><\/div>\t\t\r\n\t<\/li>\r\n\r\n<li>\r\n\r\n<p><strong>Fei BW<\/strong> (Corresponding author), Flask C, Wang H, Pi A, Wilson D, Shillingford J, Murcia N, Weimbs T, Duerk J. Image segmentation, registration and visualization of serial MR images for therapeutic assessment of polycystic kidney disease in transgenic mice. Proceedings of IEEE Engineering in Medicine and Biology Society 2005; 1:467-9. PubMed PMID:17282217.<\/p>\n \r\n\r\n\r\n\r\n\r\nFei_2006_IEEE_1\r\n\r\n\t\r\n\r\n<span class=\"pdf\">[<a target=\"_blank\" href=\"https:\/\/www.ncbi.nlm.nih.gov\/pubmed\/17282217\" rel=\"noopener noreferrer\">PubMed<\/a>]<\/span>\r\n\r\n\r\n\t<span class=\"pdf\">[<a target=\"_blank\" href=\"https:\/\/fei-lab.org\/wp-content\/uploads\/2019\/09\/Fei_2005_IEEE_Polycystic_kidney_disease.pdf\" rel=\"noopener noreferrer\">PDF<\/a>]<\/span>\r\n\r\n\r\n\r\n\t[<a download target=\"_blank\" href=\"https:\/\/fei-lab.org\/wp-content\/uploads\/2019\/09\/Fei_2006_IEEE_1_BibTeX.txt\" rel=\"noopener noreferrer\">Bibtex<\/a>]\r\n\r\n\r\n\t[<a download target=\"_blank\" href=\"https:\/\/fei-lab.org\/wp-content\/uploads\/2019\/09\/Fei_2006_IEEE_1_EndNote.enw\" rel=\"noopener noreferrer\">Endnote<\/a>]\r\n\r\n\r\n\r\n<div style=\"height: 10px; clear: both;\"><\/div>\t\t\r\n\t<\/li>\r\n\r\n<li>\r\n\r\n<p><strong>Fei BW<\/strong>, Zhuang T. Computer-assisted surgery and its localization methods. Foreign Medical Science 1997; 20:199-204.<\/p>\n \r\n\r\n\r\n\r\n\r\n\t\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n<div style=\"height: 10px; clear: both;\"><\/div>\t\t\r\n\t<\/li>\r\n\r\n<\/ul>\r\n\n<\/div><\/section>\n","protected":false},"excerpt":{"rendered":"","protected":false},"author":1,"featured_media":4322,"comment_status":"closed","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[28,2],"tags":[],"class_list":["post-832","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-main-research-post","category-research"],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v27.9 - https:\/\/yoast.com\/product\/yoast-seo-wordpress\/ -->\n<title>Quantitative Imaging Analysis &#8226; Quantitative Bioimaging Laboratory<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/fei-lab.org\/quantitative-imaging-analysis\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Quantitative Imaging Analysis &#8226; Quantitative Bioimaging Laboratory\" \/>\n<meta property=\"og:url\" content=\"https:\/\/fei-lab.org\/quantitative-imaging-analysis\/\" \/>\n<meta property=\"og:site_name\" content=\"Quantitative Bioimaging Laboratory\" \/>\n<meta property=\"article:published_time\" content=\"2017-08-29T17:55:42+00:00\" \/>\n<meta property=\"article:modified_time\" content=\"2023-12-28T20:01:18+00:00\" \/>\n<meta property=\"og:image\" content=\"https:\/\/fei-lab.org\/wp-content\/uploads\/2017\/08\/FI-Quant-image-analysis-D2_Reg_result_fig-300-x-300.jpg\" \/>\n\t<meta property=\"og:image:width\" content=\"300\" \/>\n\t<meta property=\"og:image:height\" content=\"300\" \/>\n\t<meta property=\"og:image:type\" content=\"image\/jpeg\" \/>\n<meta name=\"author\" content=\"awp-admin\" \/>\n<meta name=\"twitter:card\" content=\"summary_large_image\" \/>\n<meta name=\"twitter:label1\" content=\"Written by\" \/>\n\t<meta name=\"twitter:data1\" content=\"awp-admin\" \/>\n\t<meta name=\"twitter:label2\" content=\"Est. reading time\" \/>\n\t<meta name=\"twitter:data2\" content=\"11 minutes\" \/>\n<script type=\"application\/ld+json\" class=\"yoast-schema-graph\">{\"@context\":\"https:\\\/\\\/schema.org\",\"@graph\":[{\"@type\":\"Article\",\"@id\":\"https:\\\/\\\/fei-lab.org\\\/quantitative-imaging-analysis\\\/#article\",\"isPartOf\":{\"@id\":\"https:\\\/\\\/fei-lab.org\\\/quantitative-imaging-analysis\\\/\"},\"author\":{\"name\":\"awp-admin\",\"@id\":\"https:\\\/\\\/fei-lab.org\\\/#\\\/schema\\\/person\\\/1a52ce2f3ee13559a03a32ac20076dc0\"},\"headline\":\"Quantitative Imaging Analysis\",\"datePublished\":\"2017-08-29T17:55:42+00:00\",\"dateModified\":\"2023-12-28T20:01:18+00:00\",\"mainEntityOfPage\":{\"@id\":\"https:\\\/\\\/fei-lab.org\\\/quantitative-imaging-analysis\\\/\"},\"wordCount\":2087,\"publisher\":{\"@id\":\"https:\\\/\\\/fei-lab.org\\\/#organization\"},\"image\":{\"@id\":\"https:\\\/\\\/fei-lab.org\\\/quantitative-imaging-analysis\\\/#primaryimage\"},\"thumbnailUrl\":\"https:\\\/\\\/fei-lab.org\\\/wp-content\\\/uploads\\\/2017\\\/08\\\/FI-Quant-image-analysis-D2_Reg_result_fig-300-x-300.jpg\",\"articleSection\":[\"Research Areas\",\"Research Topics\"],\"inLanguage\":\"en-US\"},{\"@type\":\"WebPage\",\"@id\":\"https:\\\/\\\/fei-lab.org\\\/quantitative-imaging-analysis\\\/\",\"url\":\"https:\\\/\\\/fei-lab.org\\\/quantitative-imaging-analysis\\\/\",\"name\":\"Quantitative Imaging Analysis &#8226; Quantitative Bioimaging Laboratory\",\"isPartOf\":{\"@id\":\"https:\\\/\\\/fei-lab.org\\\/#website\"},\"primaryImageOfPage\":{\"@id\":\"https:\\\/\\\/fei-lab.org\\\/quantitative-imaging-analysis\\\/#primaryimage\"},\"image\":{\"@id\":\"https:\\\/\\\/fei-lab.org\\\/quantitative-imaging-analysis\\\/#primaryimage\"},\"thumbnailUrl\":\"https:\\\/\\\/fei-lab.org\\\/wp-content\\\/uploads\\\/2017\\\/08\\\/FI-Quant-image-analysis-D2_Reg_result_fig-300-x-300.jpg\",\"datePublished\":\"2017-08-29T17:55:42+00:00\",\"dateModified\":\"2023-12-28T20:01:18+00:00\",\"breadcrumb\":{\"@id\":\"https:\\\/\\\/fei-lab.org\\\/quantitative-imaging-analysis\\\/#breadcrumb\"},\"inLanguage\":\"en-US\",\"potentialAction\":[{\"@type\":\"ReadAction\",\"target\":[\"https:\\\/\\\/fei-lab.org\\\/quantitative-imaging-analysis\\\/\"]}]},{\"@type\":\"ImageObject\",\"inLanguage\":\"en-US\",\"@id\":\"https:\\\/\\\/fei-lab.org\\\/quantitative-imaging-analysis\\\/#primaryimage\",\"url\":\"https:\\\/\\\/fei-lab.org\\\/wp-content\\\/uploads\\\/2017\\\/08\\\/FI-Quant-image-analysis-D2_Reg_result_fig-300-x-300.jpg\",\"contentUrl\":\"https:\\\/\\\/fei-lab.org\\\/wp-content\\\/uploads\\\/2017\\\/08\\\/FI-Quant-image-analysis-D2_Reg_result_fig-300-x-300.jpg\",\"width\":300,\"height\":300},{\"@type\":\"BreadcrumbList\",\"@id\":\"https:\\\/\\\/fei-lab.org\\\/quantitative-imaging-analysis\\\/#breadcrumb\",\"itemListElement\":[{\"@type\":\"ListItem\",\"position\":1,\"name\":\"Home\",\"item\":\"https:\\\/\\\/fei-lab.org\\\/\"},{\"@type\":\"ListItem\",\"position\":2,\"name\":\"Quantitative Imaging Analysis\"}]},{\"@type\":\"WebSite\",\"@id\":\"https:\\\/\\\/fei-lab.org\\\/#website\",\"url\":\"https:\\\/\\\/fei-lab.org\\\/\",\"name\":\"Quantitative Bioimaging Laboratory\",\"description\":\"Quantitative Bioimaging Laboratory at the University of Texas at Dallas and UT Southwestern Medical Center\",\"publisher\":{\"@id\":\"https:\\\/\\\/fei-lab.org\\\/#organization\"},\"potentialAction\":[{\"@type\":\"SearchAction\",\"target\":{\"@type\":\"EntryPoint\",\"urlTemplate\":\"https:\\\/\\\/fei-lab.org\\\/?s={search_term_string}\"},\"query-input\":{\"@type\":\"PropertyValueSpecification\",\"valueRequired\":true,\"valueName\":\"search_term_string\"}}],\"inLanguage\":\"en-US\"},{\"@type\":\"Organization\",\"@id\":\"https:\\\/\\\/fei-lab.org\\\/#organization\",\"name\":\"Quantitative Bioimaging Laboratory\",\"url\":\"https:\\\/\\\/fei-lab.org\\\/\",\"logo\":{\"@type\":\"ImageObject\",\"inLanguage\":\"en-US\",\"@id\":\"https:\\\/\\\/fei-lab.org\\\/#\\\/schema\\\/logo\\\/image\\\/\",\"url\":\"https:\\\/\\\/fei-lab.org\\\/wp-content\\\/uploads\\\/2017\\\/08\\\/FI-cardiac-300-x-300.png\",\"contentUrl\":\"https:\\\/\\\/fei-lab.org\\\/wp-content\\\/uploads\\\/2017\\\/08\\\/FI-cardiac-300-x-300.png\",\"width\":300,\"height\":300,\"caption\":\"Quantitative Bioimaging Laboratory\"},\"image\":{\"@id\":\"https:\\\/\\\/fei-lab.org\\\/#\\\/schema\\\/logo\\\/image\\\/\"}},{\"@type\":\"Person\",\"@id\":\"https:\\\/\\\/fei-lab.org\\\/#\\\/schema\\\/person\\\/1a52ce2f3ee13559a03a32ac20076dc0\",\"name\":\"awp-admin\",\"image\":{\"@type\":\"ImageObject\",\"inLanguage\":\"en-US\",\"@id\":\"https:\\\/\\\/secure.gravatar.com\\\/avatar\\\/d5eaa37f4473e1cd7bc8b9cda51f1fcb3949681527d44543e14f936d17262077?s=96&d=mm&r=g\",\"url\":\"https:\\\/\\\/secure.gravatar.com\\\/avatar\\\/d5eaa37f4473e1cd7bc8b9cda51f1fcb3949681527d44543e14f936d17262077?s=96&d=mm&r=g\",\"contentUrl\":\"https:\\\/\\\/secure.gravatar.com\\\/avatar\\\/d5eaa37f4473e1cd7bc8b9cda51f1fcb3949681527d44543e14f936d17262077?s=96&d=mm&r=g\",\"caption\":\"awp-admin\"}}]}<\/script>\n<!-- \/ Yoast SEO plugin. -->","yoast_head_json":{"title":"Quantitative Imaging Analysis &#8226; Quantitative Bioimaging Laboratory","robots":{"index":"index","follow":"follow","max-snippet":"max-snippet:-1","max-image-preview":"max-image-preview:large","max-video-preview":"max-video-preview:-1"},"canonical":"https:\/\/fei-lab.org\/quantitative-imaging-analysis\/","og_locale":"en_US","og_type":"article","og_title":"Quantitative Imaging Analysis &#8226; Quantitative Bioimaging Laboratory","og_url":"https:\/\/fei-lab.org\/quantitative-imaging-analysis\/","og_site_name":"Quantitative Bioimaging Laboratory","article_published_time":"2017-08-29T17:55:42+00:00","article_modified_time":"2023-12-28T20:01:18+00:00","og_image":[{"width":300,"height":300,"url":"https:\/\/fei-lab.org\/wp-content\/uploads\/2017\/08\/FI-Quant-image-analysis-D2_Reg_result_fig-300-x-300.jpg","type":"image\/jpeg"}],"author":"awp-admin","twitter_card":"summary_large_image","twitter_misc":{"Written by":"awp-admin","Est. reading time":"11 minutes"},"schema":{"@context":"https:\/\/schema.org","@graph":[{"@type":"Article","@id":"https:\/\/fei-lab.org\/quantitative-imaging-analysis\/#article","isPartOf":{"@id":"https:\/\/fei-lab.org\/quantitative-imaging-analysis\/"},"author":{"name":"awp-admin","@id":"https:\/\/fei-lab.org\/#\/schema\/person\/1a52ce2f3ee13559a03a32ac20076dc0"},"headline":"Quantitative Imaging Analysis","datePublished":"2017-08-29T17:55:42+00:00","dateModified":"2023-12-28T20:01:18+00:00","mainEntityOfPage":{"@id":"https:\/\/fei-lab.org\/quantitative-imaging-analysis\/"},"wordCount":2087,"publisher":{"@id":"https:\/\/fei-lab.org\/#organization"},"image":{"@id":"https:\/\/fei-lab.org\/quantitative-imaging-analysis\/#primaryimage"},"thumbnailUrl":"https:\/\/fei-lab.org\/wp-content\/uploads\/2017\/08\/FI-Quant-image-analysis-D2_Reg_result_fig-300-x-300.jpg","articleSection":["Research Areas","Research Topics"],"inLanguage":"en-US"},{"@type":"WebPage","@id":"https:\/\/fei-lab.org\/quantitative-imaging-analysis\/","url":"https:\/\/fei-lab.org\/quantitative-imaging-analysis\/","name":"Quantitative Imaging Analysis &#8226; Quantitative Bioimaging Laboratory","isPartOf":{"@id":"https:\/\/fei-lab.org\/#website"},"primaryImageOfPage":{"@id":"https:\/\/fei-lab.org\/quantitative-imaging-analysis\/#primaryimage"},"image":{"@id":"https:\/\/fei-lab.org\/quantitative-imaging-analysis\/#primaryimage"},"thumbnailUrl":"https:\/\/fei-lab.org\/wp-content\/uploads\/2017\/08\/FI-Quant-image-analysis-D2_Reg_result_fig-300-x-300.jpg","datePublished":"2017-08-29T17:55:42+00:00","dateModified":"2023-12-28T20:01:18+00:00","breadcrumb":{"@id":"https:\/\/fei-lab.org\/quantitative-imaging-analysis\/#breadcrumb"},"inLanguage":"en-US","potentialAction":[{"@type":"ReadAction","target":["https:\/\/fei-lab.org\/quantitative-imaging-analysis\/"]}]},{"@type":"ImageObject","inLanguage":"en-US","@id":"https:\/\/fei-lab.org\/quantitative-imaging-analysis\/#primaryimage","url":"https:\/\/fei-lab.org\/wp-content\/uploads\/2017\/08\/FI-Quant-image-analysis-D2_Reg_result_fig-300-x-300.jpg","contentUrl":"https:\/\/fei-lab.org\/wp-content\/uploads\/2017\/08\/FI-Quant-image-analysis-D2_Reg_result_fig-300-x-300.jpg","width":300,"height":300},{"@type":"BreadcrumbList","@id":"https:\/\/fei-lab.org\/quantitative-imaging-analysis\/#breadcrumb","itemListElement":[{"@type":"ListItem","position":1,"name":"Home","item":"https:\/\/fei-lab.org\/"},{"@type":"ListItem","position":2,"name":"Quantitative Imaging Analysis"}]},{"@type":"WebSite","@id":"https:\/\/fei-lab.org\/#website","url":"https:\/\/fei-lab.org\/","name":"Quantitative Bioimaging Laboratory","description":"Quantitative Bioimaging Laboratory at the University of Texas at Dallas and UT Southwestern Medical Center","publisher":{"@id":"https:\/\/fei-lab.org\/#organization"},"potentialAction":[{"@type":"SearchAction","target":{"@type":"EntryPoint","urlTemplate":"https:\/\/fei-lab.org\/?s={search_term_string}"},"query-input":{"@type":"PropertyValueSpecification","valueRequired":true,"valueName":"search_term_string"}}],"inLanguage":"en-US"},{"@type":"Organization","@id":"https:\/\/fei-lab.org\/#organization","name":"Quantitative Bioimaging Laboratory","url":"https:\/\/fei-lab.org\/","logo":{"@type":"ImageObject","inLanguage":"en-US","@id":"https:\/\/fei-lab.org\/#\/schema\/logo\/image\/","url":"https:\/\/fei-lab.org\/wp-content\/uploads\/2017\/08\/FI-cardiac-300-x-300.png","contentUrl":"https:\/\/fei-lab.org\/wp-content\/uploads\/2017\/08\/FI-cardiac-300-x-300.png","width":300,"height":300,"caption":"Quantitative Bioimaging Laboratory"},"image":{"@id":"https:\/\/fei-lab.org\/#\/schema\/logo\/image\/"}},{"@type":"Person","@id":"https:\/\/fei-lab.org\/#\/schema\/person\/1a52ce2f3ee13559a03a32ac20076dc0","name":"awp-admin","image":{"@type":"ImageObject","inLanguage":"en-US","@id":"https:\/\/secure.gravatar.com\/avatar\/d5eaa37f4473e1cd7bc8b9cda51f1fcb3949681527d44543e14f936d17262077?s=96&d=mm&r=g","url":"https:\/\/secure.gravatar.com\/avatar\/d5eaa37f4473e1cd7bc8b9cda51f1fcb3949681527d44543e14f936d17262077?s=96&d=mm&r=g","contentUrl":"https:\/\/secure.gravatar.com\/avatar\/d5eaa37f4473e1cd7bc8b9cda51f1fcb3949681527d44543e14f936d17262077?s=96&d=mm&r=g","caption":"awp-admin"}}]}},"_links":{"self":[{"href":"https:\/\/fei-lab.org\/wp-json\/wp\/v2\/posts\/832","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/fei-lab.org\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/fei-lab.org\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/fei-lab.org\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/fei-lab.org\/wp-json\/wp\/v2\/comments?post=832"}],"version-history":[{"count":20,"href":"https:\/\/fei-lab.org\/wp-json\/wp\/v2\/posts\/832\/revisions"}],"predecessor-version":[{"id":5223,"href":"https:\/\/fei-lab.org\/wp-json\/wp\/v2\/posts\/832\/revisions\/5223"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/fei-lab.org\/wp-json\/wp\/v2\/media\/4322"}],"wp:attachment":[{"href":"https:\/\/fei-lab.org\/wp-json\/wp\/v2\/media?parent=832"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/fei-lab.org\/wp-json\/wp\/v2\/categories?post=832"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/fei-lab.org\/wp-json\/wp\/v2\/tags?post=832"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}