{"id":148,"date":"2017-03-17T09:37:06","date_gmt":"2017-03-17T09:37:06","guid":{"rendered":"https:\/\/fei-lab.org\/?p=148"},"modified":"2023-12-28T20:01:28","modified_gmt":"2023-12-28T20:01:28","slug":"clinical-imaging","status":"publish","type":"post","link":"https:\/\/fei-lab.org\/clinical-imaging\/","title":{"rendered":"Clinical Imaging"},"content":{"rendered":"\n<style type=\"text\/css\" data-created_by=\"avia_inline_auto\" id=\"style-css-av-av_heading-b6dc40d8e27e3ae956aa6aa03762fff0\">\n#top .av-special-heading.av-av_heading-b6dc40d8e27e3ae956aa6aa03762fff0{\npadding-bottom:10px;\n}\nbody .av-special-heading.av-av_heading-b6dc40d8e27e3ae956aa6aa03762fff0 .av-special-heading-tag .heading-char{\nfont-size:25px;\n}\n.av-special-heading.av-av_heading-b6dc40d8e27e3ae956aa6aa03762fff0 .av-subheading{\nfont-size:15px;\n}\n<\/style>\n<div  class='av-special-heading av-av_heading-b6dc40d8e27e3ae956aa6aa03762fff0 av-special-heading-h3 blockquote modern-quote  avia-builder-el-0  el_before_av_textblock  avia-builder-el-first '><h3 class='av-special-heading-tag '  itemprop=\"headline\"  >Clinical Imaging<\/h3><div class='av-subheading av-subheading_below'><p>Combined CT, PET and ultrasound images could help diagnose gynecological cancers<\/p>\n<\/div><div class=\"special-heading-border\"><div class=\"special-heading-inner-border\"><\/div><\/div><\/div>\n<section  class='av_textblock_section av-7k7hw-35e41110240973beec3e9d9cf879ccc9 '   itemscope=\"itemscope\" itemtype=\"https:\/\/schema.org\/BlogPosting\" itemprop=\"blogPost\" ><div class='avia_textblock'  itemprop=\"text\" ><p>A method of automatically combining CT, PET, and ultrasound scans into one image may help clinicians diagnose gynecological cancers. Together, the three modalities provide a clearer picture of indeterminate solid masses in the pelvic area.<br \/>\nUltrasound is the gold standard for examining abnormal pelvic masses to differentiate between a cyst and a solid mass. It cannot differentiate, however, between a benign solid mass and a malignant one. PET scans showing metabolism and blood flow within an area can provide more information about malignancy, but localizing the pathology can be a problem without CT. Putting PET\/CT and ultrasound scans together yields an image with the benefits of both.<\/p>\n<div id=\"attachment_150\" style=\"width: 310px\" class=\"wp-caption alignleft\"><img loading=\"lazy\" decoding=\"async\" aria-describedby=\"caption-attachment-150\" class=\"size-medium wp-image-150\" src=\"https:\/\/fei-lab.org\/wp-content\/uploads\/2017\/03\/Ultrasound_PET_CT-300x266.jpg\" alt=\"\" width=\"300\" height=\"266\" srcset=\"https:\/\/fei-lab.org\/wp-content\/uploads\/2017\/03\/Ultrasound_PET_CT-300x266.jpg 300w, https:\/\/fei-lab.org\/wp-content\/uploads\/2017\/03\/Ultrasound_PET_CT.jpg 354w\" sizes=\"auto, (max-width: 300px) 100vw, 300px\" \/><p id=\"caption-attachment-150\" class=\"wp-caption-text\">Fusion of PET, CT, and ultrasound images for improved diagnosis of gynecological cancer. Top: PET, CT, and ultrasound images of same patient. Bottom: Three-D fusion of three imaging modalities. Combined PET\/CT provides both anatomic (bone in white) and pathologic (tumor in red) information. At right, ultrasound image registered with PET\/CT demonstrates malignancy of mass in pelvic area.<\/p><\/div>\n<p><strong><a class=\"link\" href=\"http:\/\/feilab.org\/baoweifei.html\">Dr. Baowei Fei<\/a><\/strong> and colleagues described their method of adding ultrasound to an already-fused PET\/CT image in a paper presented at the 2007 American Institute of Ultrasound in Medicine meeting last month in New York City.<\/p>\n<p>By registering ultrasound with CT, the researchers automatically registered the ultrasound with the preregistered PET scan as well, providing a level of functional imaging over the two sources of anatomic imaging. The researchers used a slice-to-volume registration method previously applied to MR images.<\/p>\n<p>&#8220;As each image may have thousands or millions of pixels, the intensity values of the pixels represent the information of the image. We use the rich information from the intensity values to compute the mutual information. If two images are registered, their mutual information value is maximized. &#8230; This method is fully automatic and does not need landmarks for the registration,&#8221; Fei said.<br \/>\nThe computations take only seconds on a desktop computer, and Fei thinks real-time registration could be done with powerful enough hardware.<\/p>\n<p>The researchers tested their approach in 100 simulated trials, using real clinical data with variations in noise levels and image contrast. They had a success rate of 98% with a mean error of less than 0.1 mm for translation and 0.1\u00b0 rotation. They also tested the technique on a cervical cancer patient, and visual inspection indicated excellent registration. Since publishing the paper, the researchers have tried their procedure on additional patient data and anatomic targets such as the prostate.<br \/>\n&#8220;The method will provide a powerful tool for clinical applications,&#8221; Fei said. &#8220;Combining ultrasound and PET\/CT images has great potential to detect cancer at an early stage, improve the sensitivity and specificity, and better diagnose normal and malignant tissues.&#8221;<\/p>\n<p>He also looked forward to its use with image-guided radiation therapy and image-guided radiofrequency ablation.<br \/>\nFei&#8217;s colleagues contributing to the study included Drs. Nami Azar, Peter Faulhaber, Paul Rochon and Dean Nakamoto.<\/p>\n<p>Article by Wendy Despain, Diagnostic Imaging<\/p>\n<\/div><\/section>\n\n<style type=\"text\/css\" data-created_by=\"avia_inline_auto\" id=\"style-css-av-ls8d8-4d7af336118118bf20faab8f54b40de9\">\n.flex_column.av-ls8d8-4d7af336118118bf20faab8f54b40de9{\nborder-radius:0px 0px 0px 0px;\npadding:0px 0px 0px 0px;\n}\n<\/style>\n<div  class='flex_column av-ls8d8-4d7af336118118bf20faab8f54b40de9 av_one_full  avia-builder-el-2  el_after_av_textblock  el_before_av_heading  first flex_column_div av-zero-column-padding  column-top-margin'     ><section  class='av_textblock_section av-k5ph15a9-1d33740448ba180b389388841d85023b '   itemscope=\"itemscope\" itemtype=\"https:\/\/schema.org\/BlogPosting\" itemprop=\"blogPost\" ><div class='avia_textblock'  itemprop=\"text\" ><div style=\"width: 480px;\" class=\"wp-video\"><video class=\"wp-video-shortcode\" id=\"video-148-1\" width=\"480\" height=\"360\" preload=\"metadata\" controls=\"controls\"><source type=\"video\/mp4\" src=\"https:\/\/fei-lab.org\/wp-content\/uploads\/2017\/03\/Ultrasound_CT_Fusion.mp4?_=1\" \/><a href=\"https:\/\/fei-lab.org\/wp-content\/uploads\/2017\/03\/Ultrasound_CT_Fusion.mp4\">https:\/\/fei-lab.org\/wp-content\/uploads\/2017\/03\/Ultrasound_CT_Fusion.mp4<\/a><\/video><\/div>\n<p style=\"text-align: center;\">Fusion of PET, CT and Ultrasound<\/p>\n<\/div><\/section><\/div>\n\n<style type=\"text\/css\" data-created_by=\"avia_inline_auto\" id=\"style-css-av-av_heading-2cb352fc797eb1e5645b1a988c9d29ec\">\n#top .av-special-heading.av-av_heading-2cb352fc797eb1e5645b1a988c9d29ec{\npadding-bottom:10px;\n}\nbody .av-special-heading.av-av_heading-2cb352fc797eb1e5645b1a988c9d29ec .av-special-heading-tag .heading-char{\nfont-size:25px;\n}\n.av-special-heading.av-av_heading-2cb352fc797eb1e5645b1a988c9d29ec .av-subheading{\nfont-size:15px;\n}\n<\/style>\n<div  class='av-special-heading av-av_heading-2cb352fc797eb1e5645b1a988c9d29ec av-special-heading-h3  avia-builder-el-4  el_after_av_one_full  el_before_av_textblock '><h3 class='av-special-heading-tag '  itemprop=\"headline\"  >Selected Publications<\/h3><div class=\"special-heading-border\"><div class=\"special-heading-inner-border\"><\/div><\/div><\/div>\n<section  class='av_textblock_section av-k5pgykca-0c76774773aeb2fa75031ce71c9d28eb '   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>Chaudhary U, Leitch K, Chhabra A, Kohli A, <strong>Fei B<\/strong>. Deep learning-based abdominal muscle segmentation on CT images of surgical patient populations. In Medical Imaging 2022: Biomedical Applications in Molecular, Structural, and Functional Imaging 2022 Apr 4 (Vol. 12036, pp. 453-459). SPIE.<\/p>\n \r\n\r\n\r\n\r\n\r\nFei_2022_SPIE_Usamah_CT_Segmentation\r\n\r\n\t\r\n\r\n<span class=\"pdf\">[<a target=\"_blank\" href=\"https:\/\/www.ncbi.nlm.nih.gov\/pubmed\/36845411\" 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_Usamah_CT_Segmentation.pdf\" rel=\"noopener noreferrer\">PDF<\/a>]<\/span>\r\n\r\n\r\n\t[<a target=\"_blank\" href=\"https:\/\/doi.org\/10.1117\/12.2611773\" 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_Usamah_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_2022_SPIE_Usamah_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>Tran MH, Ma L, Litter JV, Chen AY, <strong>Fei B<\/strong>. Thyroid carcinoma detection on whole histologic slides using hyperspectral imaging and deep learning. In Medical Imaging 2022: Digital and Computational Pathology 2022 Apr 4 (Vol. 12039, pp. 101-111). SPIE.<\/p>\n \r\n\r\n\r\n\r\n\r\nFei_2022_SPIE_Minh_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\/36798939\" 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_Minh_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\/12.2612963\" 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_Minh_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\/05\/Fei_2022_SPIE_Minh_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>Shahedi M, Dormer JD, Do QN, Xi Y, Lewis MA, Herrera CL, Spong CY, Madhuranthakam AJ, Twickler DM, <strong>Fei B<\/strong>. Automatic segmentation of uterine cavity and placenta on MR Images using deep learning. In Medical Imaging 2022: Biomedical Applications in Molecular, Structural, and Functional Imaging 2022 Apr 4 (Vol. 12036, pp. 287-293). SPIE.<\/p>\n \r\n\r\n\r\n\r\n\r\nFei_2022_SPIE_Maysam_Placenta_Segmentation\r\n\r\n\t\r\n\r\n<span class=\"pdf\">[<a target=\"_blank\" href=\"https:\/\/www.ncbi.nlm.nih.gov\/pubmed\/36798450\" 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_Maysam_Placenta_Segmentation.pdf\" rel=\"noopener noreferrer\">PDF<\/a>]<\/span>\r\n\r\n\r\n\t[<a target=\"_blank\" href=\"https:\/\/doi.org\/10.1117\/12.2613286\" 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_Maysam_Placenta_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_Maysam_Placenta_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>Ma L, Rathgeb A, Tran M, <strong>Fei B<\/strong>. Unsupervised super resolution network for hyperspectral histologic imaging. In Medical Imaging 2022: Digital and Computational Pathology 2022 Apr 4 (Vol. 12039, pp. 149-159). SPIE.<\/p>\n \r\n\r\n\r\n\r\n\r\nFei_2022_SPIE_Ling_HSI_Super_Resolution\r\n\r\n\t\r\n\r\n<span class=\"pdf\">[<a target=\"_blank\" href=\"https:\/\/www.ncbi.nlm.nih.gov\/pubmed\/36793770\" 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_Ling_HSI_Super_Resolution.pdf\" rel=\"noopener noreferrer\">PDF<\/a>]<\/span>\r\n\r\n\r\n\t[<a target=\"_blank\" href=\"https:\/\/doi.org\/10.1117\/12.2611889\" 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_Ling_HSI_Super_Resolution_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_Ling_HSI_Super_Resolution_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>Modir N, Shahedi M, Dormer J, Ma L, Ghaderi M, Sirsi S, Cheng YS, <strong>Fei B<\/strong>. LED-based hyperspectral endoscopic imaging. In Optical Biopsy XX: Toward Real-Time Spectroscopic Imaging and Diagnosis 2022 Mar 2 (Vol. 11954, pp. 47-58). SPIE.<\/p>\n \r\n\r\n\r\n\r\n\r\nFei_2022_Photoncis_Naeeme_LED_Endoscope\r\n\r\n\t\r\n\r\n<span class=\"pdf\">[<a target=\"_blank\" href=\"https:\/\/www.ncbi.nlm.nih.gov\/pubmed\/36794092\" 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_Photoncis_Naeeme_LED_Endoscope.pdf\" rel=\"noopener noreferrer\">PDF<\/a>]<\/span>\r\n\r\n\r\n\t[<a target=\"_blank\" href=\"https:\/\/doi.org\/10.1117\/12.2609023\" 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_Photoncis_Naeeme_LED_Endoscope_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_Photoncis_Naeeme_LED_Endoscope_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>Reilly CM, Higgins M, Butler J, Esiashvili N, <strong>Fei B<\/strong>, Flynn T, Dormer JD, Schreibmann E. The Contribution of Thoracic Radiation Dose Volumes to Subsequent Development of Cardiovascular Disease in Cancer Survivors. 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The effect of image annotation with minimal manual interaction for semiautomatic prostate segmentation in CT images using fully convolutional neural networks. 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Ct image harmonization for enhancing radiomics studies. In2021 IEEE International Conference on Bioinformatics and Biomedicine (BIBM) 2021 Dec 9 (pp. 1057-1062). 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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>Shahedi M, Spong CY, Dormer JD, Do QN, Xi Y, Lewis MA, Herrera C, Madhuranthakam AJ, Twickler DM, <strong>Fei B<\/strong>. Deep learning-based segmentation of the placenta and uterus on MR images. 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Automatic segmentation of the prostate on MR images based on anatomy and deep learning. In Medical Imaging 2021: Image-Guided Procedures, Robotic Interventions, and Modeling 2021 Feb 15 (Vol. 11598, p. 115981N). 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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>Caughlin K, Shahedi M, Shoag JE, Barbieri C, Margolis D, <strong>Fei B<\/strong>. Three-dimensional prostate CT segmentation through fine-tuning of a pre-trained neural network using no reference labeling. InMedical Imaging 2021: Image-Guided Procedures, Robotic Interventions, and Modeling 2021 Feb 15 (Vol. 11598, p. 115980L). 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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<div class=\"gs_citr\" tabindex=\"0\">Yogananda CG, Shah BR, Yu FF, Pinho MC, Nalawade SS, Murugesan GK, Wagner BC, Mickey B, Patel TR, <strong>Fei BW<\/strong>, Madhuranthakam AJ. A novel fully automated MRI-based deep-learning method for classification of 1p\/19q co-deletion status in brain gliomas. Neuro-oncology advances. 2020 Jan; 2(1):vdaa066.<\/div>\n \r\n\r\n\r\n\r\n\r\nYogananda_2020_NeuroOnc_1\r\n\r\n\t\r\n\r\n<span class=\"pdf\">[<a target=\"_blank\" href=\"https:\/\/www.ncbi.nlm.nih.gov\/pubmed\/32705083\" 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_NeuroOnc_Yogananda_MRI_Deep_learning_Codeletion.pdf\" rel=\"noopener noreferrer\">PDF<\/a>]<\/span>\r\n\r\n\r\n\t[<a target=\"_blank\" href=\"https:\/\/doi.org\/10.1093\/noajnl\/vdaa066\" 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_NeuroOnc_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\/2020\/09\/Yogananda_2020_NeuroOnc_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<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<p>Tian Z, Li X, Zheng Y, Chen Z, Shi Z, Liu L, <strong>Fei B<\/strong>. Graph\u2010convolutional\u2010network\u2010based interactive prostate segmentation in MR images. Medical physics. 2020 Sep;47(9):4164-76.<\/p>\n \r\n\r\n\r\n\r\n\r\nFei_2020_Tian_MP_Prostate_Segmentation\r\n\r\n\t\r\n\r\n<span class=\"pdf\">[<a target=\"_blank\" href=\"https:\/\/www.ncbi.nlm.nih.gov\/pubmed\/32533855\" 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_2020_Tian_MP_Prostate_Segmentation.pdf\" rel=\"noopener noreferrer\">PDF<\/a>]<\/span>\r\n\r\n\r\n\t[<a target=\"_blank\" href=\"https:\/\/doi.org\/10.1002\/mp.14327\" 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_2020_Tian_MP_Prostate_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_2020_Tian_MP_Prostate_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>Yogananda CG, Shah BR, Nalawade S, Murugesan GK, Frank FY, Pinho MC, Wagner BC, Mickey B, Patel TR, <strong>Fei BW<\/strong>, Madhuranthakam AJ, Maldjian JA. MRI-based deep learning method for determining methylation status of the 06 methylguanne DNA methyltransferase promoter outperforms tissue based methods in brain gliomas. bioRxiv 2020.<\/p>\n \r\n\r\n\r\n\r\n\r\nYogananda_2020_bioRxiv\r\n\r\n\t\r\n\r\n<span class=\"pdf\">[<a target=\"_blank\" href=\"https:\/\/www.ncbi.nlm.nih.gov\/pubmed\/37760146\" 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_Maldjian_MRI_DL_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\/2020\/06\/Yogananda_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\/Yogananda_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<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<div class=\"gs_citr\" tabindex=\"0\">Zhou X, Ma L, Halicek M, Dormer J, <strong>Fei BW <\/strong>(Corresponding author). Development of a new polarized hyperspectral imaging microscope. Imaging, Therapeutics, and Advanced Technology in Head and Neck Surgery and Otolaryngology 2020; 11213(1121308). International Society for Optics and Photonics,<\/div>\n \r\n\r\n\r\n\r\n\r\nZhou_2020_Otolaryngology\r\n\r\n\t\r\n\r\n<span class=\"pdf\">[<a target=\"_blank\" href=\"https:\/\/www.ncbi.nlm.nih.gov\/pubmed\/32577044\" 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_Ximing_PHSI_1121308.pdf\" rel=\"noopener noreferrer\">PDF<\/a>]<\/span>\r\n\r\n\r\n\t[<a target=\"_blank\" href=\"https:\/\/doi.org\/10.1117\/12.2549676\" 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\/Zhou_2020_Otolaryngology_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\/Zhou_2020_Otolaryngology_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\">Ma L, Liu X, <strong>Fei BW<\/strong>. A multi-level similarity measure for the retrieval of the common CT imaging signs of lung diseases. Medical &amp; Biological Engineering &amp; Computing; 2020:1-15.<\/div>\n \r\n\r\n\r\n\r\n\r\nMa_2020_MBEC\r\n\r\n\t\r\n\r\n<span class=\"pdf\">[<a target=\"_blank\" href=\"https:\/\/www.ncbi.nlm.nih.gov\/pubmed\/32124223\" 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_Ma_Lung_Similarity.pdf\" rel=\"noopener noreferrer\">PDF<\/a>]<\/span>\r\n\r\n\r\n\t[<a target=\"_blank\" href=\"https:\/\/doi.org\/10.1007\/s11517-020-02146-4\" 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_MBEC_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_MBEC_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, 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>Ortega S, Halicek M, Fabelo H, Guerra R, Lopez C, Lejeune M, Godtliebsen F, Callico GM, <strong>Fei BW <\/strong>(Corresponding author). Hyperspectral imaging and deep learning for the detection of breast cancer cells in digitized histological images. Medical Imaging 2020: Digital Pathology; 11320(113200V). International Society for Optics and Photonics,<\/p>\n \r\n\r\n\r\n\r\n\r\nOrtega_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\/32528219\" 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_Orgega_HSI_113200V.pdf\" rel=\"noopener noreferrer\">PDF<\/a>]<\/span>\r\n\r\n\r\n\t[<a target=\"_blank\" href=\"https:\/\/doi.org\/10.1117\/12.2548609\" 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_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\/Ortega_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>Dormer J, Bhuiyan M, Rahman N, Deaton N, Sheng J, Padala M, Desai J, <strong>Fei BW <\/strong>(Corresponding author). Image guided mitral valve replacement: registration of 3D ultrasound and 2D x-ray images. Medical Imaging 2020: Image-Guided Procedures, Robotic Interventions, and Modeling; 11315(113150Z). International Society for Optics and Photonics,<\/p>\n \r\n\r\n\r\n\r\n\r\nDormer_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\/32528217\" 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_Dormer_Heart_Registration_113150Z.pdf\" rel=\"noopener noreferrer\">PDF<\/a>]<\/span>\r\n\r\n\r\n\t[<a target=\"_blank\" href=\"https:\/\/doi.org\/10.1117\/12.2549407\" 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\/Dormer_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\/Dormer_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>Pfefferle M, Shahub S, Shahedi M, Gahan J, Johnson B, Le P, Vargas J, Judson B, Alshara Y, <strong>Fei BW<\/strong> (Corresponding author). Renal biopsy under augmented reality guidance. Medical Imaging 2020: Image-Guided Procedures, Robotic Interventions, and Modeling; 11315(113152W). International Society for Optics and Photonics,<\/p>\n \r\n\r\n\r\n\r\n\r\nPfefferle_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_Matthew_AR_113152W.pdf\" rel=\"noopener noreferrer\">PDF<\/a>]<\/span>\r\n\r\n\r\n\t[<a target=\"_blank\" href=\"https:\/\/doi.org\/10.1117\/12.2550593\" 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\/Pfefferle_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\/Pfefferle_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>Selim M, Zhang J, <strong>Fei BW<\/strong>, Zhang GQ, Chen J. STAN-CT: Standardizing CT Image using Generative Adversarial Network. arXiv preprint; arXiv: 2004.01307.<\/p>\n \r\n\r\n\r\n\r\n\r\nSelim_2020_arXiv\r\n\r\n\t\r\n\r\n<span class=\"pdf\">[<a target=\"_blank\" href=\"https:\/\/www.ncbi.nlm.nih.gov\/pubmed\/33936486\" 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_arXiv_Selim_STAN_CT.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\/Selim_2020_arXiv_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\/Selim_2020_arXiv_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>Nalawade SS, Muregesan GK, Vejdani-Jahromi M, Fisicaro RA, Yogananda CGB, Wagner B, Mickey B, Maher E, Pinho MC, <strong>Fei BW<\/strong>. Classification of Brain Tumor IDH Status using MRI and Deep Learning. bioRxiv (2019):757344.<\/p>\n \r\n\r\n\r\n\r\n\r\nNalawade_2019_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\/2019\/01\/Fei_2019_bioRxiv_Nalawade_Classification_Brain_tumor.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\/Nalawade_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\/Nalawade_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>Brown K, Dormer JD, <strong>Fei BW<\/strong>, Hoyt K, Ruiter NV, Byram BC. Deep 3D convolutional neural networks for fast super-resolution ultrasound imaging. Proceedings of SPIE Medical Imaging 2019: Ultrasonic Imaging and Tomography.<\/p>\n \r\n\r\n\r\n\r\n\r\nBrown_2019_MedImaging\r\n\r\n\t\r\n\r\n<span class=\"pdf\">[<a target=\"_blank\" href=\"https:\/\/www.ncbi.nlm.nih.gov\/pubmed\/32476699\" 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_2019_SPIE_Brown_Deep_3D_CNN_Ultrasound.pdf\" rel=\"noopener noreferrer\">PDF<\/a>]<\/span>\r\n\r\n\r\n\t[<a target=\"_blank\" href=\"https:\/\/doi.org\/10.1117\/12.2511897\" 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\/Brown_2019_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\/Brown_2019_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>Qin X, <strong>Fei BW<\/strong>. &#8220;Cardiac Fiber Imaging with 3D Ultrasound and MR Diffusion Tensor Imaging.&#8221; In <i>Cardiovascular Imaging: An Engineering and Clinical Perspective<\/i>, edited by Ayman El-Baz. Boca Raton, FL: CRC Press, Taylor &amp; Francis Group,<\/p>\n \r\n\r\n\r\n\r\n\r\nQin_2018_CRC\r\n\r\n\t\r\n\r\n\r\n\r\n\r\n\t[<a download target=\"_blank\" href=\"https:\/\/fei-lab.org\/wp-content\/uploads\/2019\/10\/Qin_2018_CRC_BibTeX.txt\" rel=\"noopener noreferrer\">Bibtex<\/a>]\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>Dormer JD, Halicek M, Ma L, Reilly CM, Schreibmann E, <strong>Fei BW<\/strong> (Corresponding author). Convolutional neural networks for the detection of diseased hearts using CT images and left atrium patches. Proceedings of SPIE: The International Society for Optical Engineering;10575.<\/p>\n \r\n\r\n\r\n\r\n\r\nDormer_2018_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\/30197463\" 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_SPIE_Dormer_CNN_Diseased_hearts_CT_Left_atrium.pdf\" rel=\"noopener noreferrer\">PDF<\/a>]<\/span>\r\n\r\n\r\n\t[<a target=\"_blank\" href=\"https:\/\/doi.org\/10.1117\/12.2293548\" 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\/Dormer_2018_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\/Dormer_2018_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>Dormer JD, Ma L, Halicek M, Reilly CM, Schreibmann E, <strong>Fei BW<\/strong> (Corresponding author). Heart chamber segmentation from CT using convolutional neural networks. Proceedings of SPIE: The International Society for Optical Engineering;10578.<\/p>\n \r\n\r\n\r\n\r\n\r\nDormer_2018_MedImaging\r\n\r\n\t\r\n\r\n<span class=\"pdf\">[<a target=\"_blank\" href=\"https:\/\/www.ncbi.nlm.nih.gov\/pubmed\/30197464\" 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_SPIE_Dormer_Heart_chamber_segmentation_CT_CNN.pdf\" rel=\"noopener noreferrer\">PDF<\/a>]<\/span>\r\n\r\n\r\n\t[<a target=\"_blank\" href=\"https:\/\/doi.org\/10.1117\/12.2293554\" 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\/Dormer_2018_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\/Dormer_2018_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>Mei KQ, Hu B, <strong>Fei BW<\/strong>, Qin BJ. Phase asymmetry guided adaptive fractional-order total variation and diffusion for feature-preserving ultrasound despeckling. arXiv:1810.12538.<\/p>\n \r\n\r\n\r\n\r\n\r\nMei_2018_arXiv\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_arXiv_Mei_Phase_asymmetry_Variation_Ultrasound_despeckling.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\/Mei_2018_arXiv_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\/Mei_2018_arXiv_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>Abiodun-Ojo OA, <strong>Fei BW<\/strong> Nieh PT, Master VA, Akintayo A, Tade F, Akin-Akintayo O, Alemozaffar M, Osunkoya AO, Goodman MM, Schuster DM. The role of fluciclovine (18F) PET\/CT directed, 3D ultrasound-guided fusion targeted biopsy in the detection of biochemically recurrent prostate cancer. Journal of Nuclear Medicine;59(s1):1481.<\/p>\n \r\n\r\n\r\n\r\n\r\nAbiodunOjo_2018_JNM\r\n\r\n\t\r\n\r\n\r\n\r\n\r\n\t[<a download target=\"_blank\" href=\"https:\/\/fei-lab.org\/wp-content\/uploads\/2019\/09\/AbiodunOjo_2018_JNM_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\/AbiodunOjo_2018_JNM_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, Schuster, DM, <strong>Fei BW<\/strong> (Corresponding author). \u201cA Combined Learning Algorithm for Prostate Segmentation on 3D CT Images,&#8221; Medical Physics; 44(11): 5768-5781.<\/p>\n \r\n\r\n\r\n\r\n\r\nMa_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\/28834585\" 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\/01\/Fei_2017_MedPhys_Ma_Combined_learning_Prostate_3D_CT.pdf\" rel=\"noopener noreferrer\">PDF<\/a>]<\/span>\r\n\r\n\r\n\t[<a target=\"_blank\" href=\"https:\/\/doi.org\/10.1002\/mp.12528\" 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_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\/Ma_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, <strong>Fei BW<\/strong> (Corresponding author). \u201cA random walk\u2010based segmentation framework for 3D ultrasound images of the prostate,&#8221; Medical Physics, 2017 Jun 5. PubMed PMID:28582803.<\/p>\n \r\n\r\n\r\n\r\n\r\nMa_2017_MedPhys_1\r\n\r\n\t\r\n\r\n<span class=\"pdf\">[<a target=\"_blank\" href=\"https:\/\/www.ncbi.nlm.nih.gov\/pubmed\/28582803\" 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_Ma_Random_walk_Segmentation_Ultrasound_Prostate.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\/Ma_2017_MedPhys_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_MedPhys_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>Tian, ZQ., Liu, LZ., Zhang, ZF., Xue, JR., <strong>Fei, BW<\/strong>.(2017). \u201cA supervoxel-based segmentation method for prostate MR images.\u201dMedical Physics 44(2): 558-569.<\/p>\n \r\n\r\n\r\n\r\n\r\nTian_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\/26848206\" 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_Tian_Supervoxel_Segmentation_MRI.pdf\" rel=\"noopener noreferrer\">PDF<\/a>]<\/span>\r\n\r\n\r\n\t[<a target=\"_blank\" href=\"https:\/\/doi.org\/10.1002\/mp.12048\" 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_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\/Tian_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>Tian Z, Liu L, <strong>Fei BW<\/strong>, Deep convolutional neural network for prostate MR segmentation, Proc. SPIE 10135, Medical Imaging 2017: Image-Guided Procedures, Robotic Interventions, and Modeling, 101351L, March 3, 2017, Orlando, FL.<\/p>\n \r\n\r\n\r\n\r\n\r\nTian_2017_MedImaging\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_2017_SPIE_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_2017_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\/Tian_2017_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>Ma L, Liu X, <strong>Fei BW<\/strong> (Corresponding author). Learning with distribution of optimized features for recognizing common CT imaging signs of lung diseases, Physics in Medicine and Biology 2017, 62:612-632.<\/p>\n \r\n\r\n\r\n\r\n\r\nMa_2017_PMB\r\n\r\n\t\r\n\r\n<span class=\"pdf\">[<a target=\"_blank\" href=\"https:\/\/www.ncbi.nlm.nih.gov\/pubmed\/28033116\" 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_PMB_Ma_Learning_Features_CT_Lung_diseases.pdf\" rel=\"noopener noreferrer\">PDF<\/a>]<\/span>\r\n\r\n\r\n\t[<a target=\"_blank\" href=\"https:\/\/doi.org\/10.1088\/1361-6560\/62\/2\/612\" 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\/Ma_2017_PMB_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\/2017\/01\/Ma_2017_PMB_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, Tian Z, Venkataraman R, Sarkar S, Liu X, Nieh PT, Master VV, Schuster DM, <strong>Fei BW<\/strong> (Corresponding author). Random walk based segmentation for the prostate on 3D transrectal ultrasound images. Proceedings of SPIE \u2013 The International Society for Optical Engineering 2016; 9786:978607. PubMed PMID:27660383.<\/p>\n \r\n\r\n\r\n\r\n\r\nMa_2016_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\/27660383\" 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_Ma_Random_walk_Segmentation_Prostate_Transrectal_Ultrasound.pdf\" rel=\"noopener noreferrer\">PDF<\/a>]<\/span>\r\n\r\n\r\n\t[<a target=\"_blank\" href=\"https:\/\/doi.org\/10.1002\/mp.12396\" 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_2016_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_2016_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>Tian Z, Liu L, Zhang Z, <strong>Fei BW<\/strong> (Corresponding author). &#8220;Superpixel-based segmentation for 3D prostate MR images.&#8221; IEEE Transactions on Medical Imaging; 35(3):791-801.<\/p>\n \r\n\r\n\r\n\r\n\r\nTian_2016_IEEE\r\n\r\n\t\r\n\r\n<span class=\"pdf\">[<a target=\"_blank\" href=\"https:\/\/www.ncbi.nlm.nih.gov\/pubmed\/26540678\" 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_IEEE_Tian_Superpixel_Segmentation_Prostate_MRI.pdf\" rel=\"noopener noreferrer\">PDF<\/a>]<\/span>\r\n\r\n\r\n\t[<a target=\"_blank\" href=\"https:\/\/doi.org\/10.1109\/TMI.2015.2496296\" 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\/Tian_2016_IEEE_BibTeX.txt\" rel=\"noopener noreferrer\">Bibtex<\/a>]\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> (Corresponding author). A fully automatic multi-atlas based segmentation method for prostate MR images. Proceedings of SPIE \u2013 The International Society for Optical Engineering 2015; 9413:941340. PubMed PMID:26798187.<\/p>\n \r\n\r\n\r\n\r\n\r\nTian_2015_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\/26798187\" 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_Tian_Fully_automatic_multi_atlas.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_2015_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\/Tian_2015_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>Tian Z, Liu L, <strong>Fei BW<\/strong> (Corresponding author). A supervoxel-based segmentation method for prostate MR images. Proceedings of SPIE \u2013 The International Society for Optical Engineering 2015; 9413:941318. PubMed PMID:26848206.<\/p>\n \r\n\r\n\r\n\r\n\r\nTian_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\/27991675\" 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_Tian_Supervoxel_Segmentation_Prostate_MRI.pdf\" rel=\"noopener noreferrer\">PDF<\/a>]<\/span>\r\n\r\n\r\n\t[<a target=\"_blank\" href=\"https:\/\/doi.org\/10.1002\/mp.12048\" 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_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\/Tian_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>Qin X, <strong>Fei BW<\/strong> (Corresponding author). DTI template-based estimation of cardiac fiber orientations from 3D ultrasound. Medical Physics 2015; 42:2915.<\/p>\n \r\n\r\n\r\n\r\n\r\nQin_2015_MedPhys_1\r\n\r\n\t\r\n\r\n<span class=\"pdf\">[<a target=\"_blank\" href=\"https:\/\/www.ncbi.nlm.nih.gov\/pubmed\/26127045\" 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_MedPhys_Qin_DTI_Cardiac_3D_Ultrasound.pdf\" rel=\"noopener noreferrer\">PDF<\/a>]<\/span>\r\n\r\n\r\n\t[<a target=\"_blank\" href=\"https:\/\/doi.org\/10.1118\/1.4921121\" 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\/Qin_2015_MedPhys_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\/Qin_2015_MedPhys_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>Qin X, <strong>Fei BW<\/strong> (Corresponding author). Measuring myofiber orientations from high-frequency ultrasound images using multiscale decompositions. Physics in Medicine and Biology 2014; 59:3907-24.<\/p>\n \r\n\r\n\r\n\r\n\r\nQin_2014_PMB\r\n\r\n\t\r\n\r\n<span class=\"pdf\">[<a target=\"_blank\" href=\"https:\/\/www.ncbi.nlm.nih.gov\/pubmed\/24957945\" 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_PMB_Qin_Measuring_Myofiber.pdf\" rel=\"noopener noreferrer\">PDF<\/a>]<\/span>\r\n\r\n\r\n\t[<a target=\"_blank\" href=\"https:\/\/doi.org\/10.1088\/0031-9155\/59\/14\/3907\" 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\/Qin_2014_PMB_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_2014_PMB_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>, Nieh PT, Schuster DM, Master VA, Multimodality molecular imaging for targeted biopsy of prostate cancer, The 2nd International Conference of Biomedical Engineering, Beijing, China, June 13-15, 2014.<\/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>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>Qin X, Cong Z, Jiang R, Shen M, Wagner MB, Kishbom P, <strong>Fei BW<\/strong> (Corresponding author). Extracting cardiac myofiber orientations from high frequency ultrasound images. Proceedings of SPIE \u2013 The International Society for Optical Engineering 2013; 8675. PubMed PMID:24392208.<\/p>\n \r\n\r\n\r\n\r\n\r\nQin_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\/24392208\" 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_2013_SPIE_Qin_Extracting_myofiber.pdf\" rel=\"noopener noreferrer\">PDF<\/a>]<\/span>\r\n\r\n\r\n\t[<a target=\"_blank\" href=\"https:\/\/doi.org\/10.1117\/12.2006494\" 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\/Qin_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\/Qin_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). Multiscale segmentation of the skull in MR images for MRI-based attenuation correction of combined MR\/PET. Journal of the American Medical Informatics Association 2013; 20: 1037-45.<\/p>\n \r\n\r\n\r\n\r\n\r\nYang_2013_JAMIA\r\n\r\n\t\r\n\r\n<span class=\"pdf\">[<a target=\"_blank\" href=\"https:\/\/www.ncbi.nlm.nih.gov\/pubmed\/23761683\" 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_2013_Yang_Multiscale_Skull_MRI.pdf\" rel=\"noopener noreferrer\">PDF<\/a>]<\/span>\r\n\r\n\r\n\t[<a target=\"_blank\" href=\"https:\/\/doi.org\/10.1136\/amiajnl-2012-001544\" 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\/Yang_2013_JAMIA_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_2013_JAMIA_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), Nieh PT, Schuster DM, Master VA. PET directed, 3D ultrasound-guided prostate biopsy, Diagnostic Imaging Europe 2013, 12-15 (Invited Paper, Featured by the journal cover).<\/p>\n \r\n\r\n\r\n\r\n\r\nFei_2013_DiagImagingEU\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_2013_PET_3DUltrasound_Prostate.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_2013_DiagImagingEU_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_2013_DiagImagingEU_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><strong>Fei BW<\/strong>, Schuster DM, Nieh P, Akbari H, Fenster A, Master V, A molecular image-directed, 3D ultrasound-guided biopsy system for the prostate, Edited by David R. Holmes III, Kenneth H. Wong, Proceedings of SPIE \u2013 The International Society for Optical Engineering 2012, 8316, 831613-1~8.<\/p>\n \r\n\r\n\r\n\r\n\r\nFei_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\/22708023\" 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_SPIE_Biopsy.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_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\/Fei_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>Feng S, Bliznakova K, Qin X, <strong>Fei BW<\/strong>, Sechopoulos I, Characterization of the homogeneous breast tissue mixture approximation for breast image dosimetry, The Annual Meeting of the American Association of Physics in Medicine, Charlotte, NC, July 29-August 2, 2012.<\/p>\n \r\n\r\n\r\n\r\n\r\nFeng_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\/22894430\" rel=\"noopener noreferrer\">PubMed<\/a>]<\/span>\r\n\r\n\r\n\r\n\t[<a target=\"_blank\" href=\"https:\/\/doi.org\/10.1118\/1.4737025\" 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\/Feng_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\/Feng_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>Sechopoulos I, Bliznakova K, Qin X, <strong>Fei BW<\/strong>, Feng SS. Characterization of the homogeneous tissue mixture approximation in breast imaging dosimetry, Medical Physics 2012; 39:5050-5059. (2012 <strong>Best Paper Award<\/strong>, The Southeast Chapter of AAPM).<\/p>\n \r\n\r\n\r\n\r\n\r\nSechopoulos_2012_MedPhys\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_2012_Breast_CT_Dose.pdf\" rel=\"noopener noreferrer\">PDF<\/a>]<\/span>\r\n\r\n\r\n\t[<a target=\"_blank\" href=\"https:\/\/doi.org\/10.1118\/1.4737025\" 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\/Sechopoulos_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\/Sechopoulos_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>Yang X, Wu S, Sechopoulos I, <strong>Fei BW<\/strong> (Corresponding author). Cupping artifact correction and automated classification for high-resolution dedicated breast CT images. Medical Physics 2012; 39:6397-6406.<\/p>\n \r\n\r\n\r\n\r\n\r\nYang_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\/23039675\" 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_Breast_Classification.pdf\" rel=\"noopener noreferrer\">PDF<\/a>]<\/span>\r\n\r\n\r\n\t[<a target=\"_blank\" href=\"https:\/\/doi.org\/10.1118\/1.4754654\" 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\/Yang_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\/Yang_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><strong>Fei BW<\/strong>, Schuster DM, Master V, Nieh P, Incorporating PET\/CT images into 3D ultrasound-guided biopsy of the prostate. The Annual Meeting of the American Association of Physics in Medicine, Charlotte, NC, July 29-August 2, 2012.<\/p>\n \r\n\r\n\r\n\r\n\r\nFei_2012_MedPhys_1\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\/09\/Fei_2012_MedPhys_Incorporating_PET_Prostate.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_2012_MedPhys_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_2012_MedPhys_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> (Corresponding author), Yang X, Nye J, Aarovold J, Cervo M, Stark R, Meltzer CC, and Votaw J. MR\/PET quantification tools: registration, segmentation, classification, and MR-based attenuation correction, Medical Physics 2012, 39:6443-6454.<\/p>\n \r\n\r\n\r\n\r\n\r\nFei_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\/23039679\" 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_MR_PET_AC-1.pdf\" rel=\"noopener noreferrer\">PDF<\/a>]<\/span>\r\n\r\n\r\n\t[<a target=\"_blank\" href=\"https:\/\/doi.org\/10.1118\/1.4754796\" 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\/Fei_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\/Fei_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>Yang X, Ghafourian P, Sharma P, Salman K, Martin D, <strong>Fei BW<\/strong> (Corresponding author). Nonrigid registration and classification of the kidneys in 3D dynamic contrast enhanced (DCE) MR images. Proceedings of SPIE \u2013 The International Society for Optical Engineering 2012; 8314:83140B. PubMed PMID:22468206.<\/p>\n \r\n\r\n\r\n\r\n\r\nYang_2012_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\/22468206\" 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\/2012\/01\/Fei_2012_Yang_SPIE_Kidney_DCE_MRI.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_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_2012_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>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><strong>Fei BW<\/strong>, Master V, Nieh P, Akbari H, Yang X, Fenster A, Schuster D. A PET\/CT directed, 3D ultrasound-guided biopsy system for prostate cancer.Workshop on Prostate Cancer Imaging at the Annual Meeting of the Society of Medical Imaging Computing and Image Assisted Interventions (MICCAI), Toronto, Canada, September 18-22, 2011.<\/p>\n \r\n\r\n\r\n\r\n\r\nFei_2011_WorkshopProstateCancer\r\n\r\n\t\r\n\r\n<span class=\"pdf\">[<a target=\"_blank\" href=\"https:\/\/www.ncbi.nlm.nih.gov\/pubmed\/26866061\" 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_MICCAI_Biopsy_System.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_2011_WorkshopProstateCancer_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_2011_WorkshopProstateCancer_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, Sechopoulos I, <strong>Fei BW<\/strong> (Corresponding author). Automatic tissue classification for high-resolution breast CT images based on bilateral filtering. Proceedings of SPIE \u2013 The International Society for Optical Engineering 2011; 7962:79623H. PubMed PMID:24027608.<\/p>\n \r\n\r\n\r\n\r\n\r\nYang_2011_MedImaging\r\n\r\n\t\r\n\r\n<span class=\"pdf\">[<a target=\"_blank\" href=\"https:\/\/www.ncbi.nlm.nih.gov\/pubmed\/24027608\" 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_Breast_Classification.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_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_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>, A skull segmentation method for brain MR images based on multiscale bilateral filtering scheme. SPIE Medical Imaging: Image Processing, Edited by Benoit M. Dawant, David R. Haynor, Proceedings of SPIE \u2013 The International Society for Optical Engineering2010;7623:76233K-1~8.<\/p>\n \r\n\r\n\r\n\r\n\r\nYang_2010_MedImaging\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\/2010\/01\/Fei_2010_SPIE_Yang_Skull_segmentation_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\/Yang_2010_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_2010_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>Li K, <strong>Fei BW<\/strong> (Corresponding author). A deformable model-based minimal path segmentation method for kidney MR images. Proceedings of SPIE \u2013 The International Society for Optical Engineering 2008; 6914. PubMed PMID:24386528.<\/p>\n \r\n\r\n\r\n\r\n\r\nLi_2008_MedImaging\r\n\r\n\t\r\n\r\n<span class=\"pdf\">[<a target=\"_blank\" href=\"https:\/\/www.ncbi.nlm.nih.gov\/pubmed\/24386528\" 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\/2008\/01\/Fei_2008_SPIE_LiKe_deformable_segmentation_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\/Li_2008_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\/Li_2008_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>Li K, <strong>Fei BW<\/strong>, A New 3D Model-based minimal path segmentation method for kidney MR images. The 2nd International Conference on Bioinformatics and Biomedical Engineering, 2342-2344, May 16-18, 2008.<\/p>\n \r\n\r\n\r\n\r\n\r\nLi_2008_ICBBE\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\/2008\/01\/Fei_2008_IEEE_ICBBE_Model_based_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\/Li_2008_ICBBE_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\/Li_2008_ICBBE_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>Chen X, Gilkeson RC, <strong>Fei BW<\/strong> (Corresponding author). Automatic 3D-to-2D registration for CT and dual-energy digital radiography for calcification detection. Medical Physics 2007; 34:4934-4943.<\/p>\n \r\n\r\n\r\n\r\n\r\nChen_2007_MedPhys\r\n\r\n\t\r\n\r\n<span class=\"pdf\">[<a target=\"_blank\" href=\"https:\/\/www.ncbi.nlm.nih.gov\/pubmed\/18196818\" 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_2007_Med_Phys_3D_2D_registration_dual_energy_NIH.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\/Chen_2007_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\/Chen_2007_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><strong>Fei BW<\/strong>, Azar N, Greer M, Rochon PJ, Faulhaber PP. Automatic registration and fusion of ultrasound imaging and positron emission tomography (PET) for improved diagnosis of gynecologic cancer. The American Institute of Ultrasound in Medicine 2007 Convention, New York, NY, March 15-18, 2007.<\/p>\n \r\n\r\n\r\n\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_2003_IEEE_EMBS_CT_SPECT_registration.pdf\" rel=\"noopener noreferrer\">PDF<\/a>]<\/span>\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><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>Suri JS, Pappu V, Salvado O, <strong>Fei BW<\/strong>, Laxminarayan S, Zhang S, Lewin JS, Duerk JL, Wilson DL. &#8220;Accurate lumen identification, detection, and quantification in MR plaque volumes.&#8221; In <em>Handbook of Biomedical Image Analysis: Volume II: Segmentation Models<\/em>, edited by Jasjit S. Suri, David L. Wilson, Swamy Laxminarayan, 451-530. New York, NY: Kluwer Academic\/Plenum Publishers,<\/p>\n \r\n\r\n\r\n\r\n\r\nSuri_2005_JBIA\r\n\r\n\t\r\n\r\n\r\n\r\n\r\n\t[<a download target=\"_blank\" href=\"https:\/\/fei-lab.org\/wp-content\/uploads\/2019\/09\/Suri_2005_JBIA_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\/Suri_2005_JBIA_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), Duerk JL, Sodee DB, Wilson DL. Semiautomatic nonrigid registration for the prostate and pelvic MR volumes.Academic Radiology 2005; 12:815-824.<\/p>\n \r\n\r\n\r\n\r\n\r\nFei_2005_AcadRadiology\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\/2017\/03\/Fei_2005_AR_automatic_prostate_registration.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_2005_AcadRadiology_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_2005_AcadRadiology_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>, Suri JS, Wilson DL. &#8220;Three-dimensional volume registration of carotid MR images.&#8221; In <em>Plaque Imaging: Pixel to Molecular Level<\/em>, edited by Jasjit S. Suri, Chun Yuan, David L. Wilson, Swamy Laxminarayan, 294-411. Fairfax, VA: IOS Press, Inc.,<\/p>\n \r\n\r\n\r\n\r\n\r\nFei_2005_SHTI\r\n\r\n\t\r\n\r\n<span class=\"pdf\">[<a target=\"_blank\" href=\"https:\/\/www.ncbi.nlm.nih.gov\/pubmed\/15923750\" rel=\"noopener noreferrer\">PubMed<\/a>]<\/span>\r\n\r\n\r\n\r\n\r\n\t[<a download target=\"_blank\" href=\"https:\/\/fei-lab.org\/wp-content\/uploads\/2019\/09\/Fei_2005_SHTI_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_2005_SHTI_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>Suri JS, Pappu V, Salvado O, <strong>Fei BW<\/strong>, Zhang S; Lewin JS, Duerk, JL, Wilson DL. Rotational effect on ROI\u2019s for accurate lumen quantification in bifurcated MR plaque volumes. Proceedings of 17th IEEE Symposium on Computer-Based Medical Systems, Proceedings of IEEE 2004; 414- 418.<\/p>\n \r\n\r\n\r\n\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\/2004\/01\/Fei_IEEE_CMBS_Suri_MRI_Plaque_Imaging.pdf\" rel=\"noopener noreferrer\">PDF<\/a>]<\/span>\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><strong>Fei BW<\/strong>, Kemper C, Wilson DL. A comparative study of warping and rigid body registration for the prostate and pelvic MR volumes. Computerized Medical Imaging and Graphics 2003; 27:267-281.<\/p>\n \r\n\r\n\r\n\r\n\r\nFei_2003_CMIG\r\n\r\n\t\r\n\r\n<span class=\"pdf\">[<a target=\"_blank\" href=\"https:\/\/www.ncbi.nlm.nih.gov\/pubmed\/12631511\" 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_2003_CMIG_Non_rigid_prostate_registration.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_2003_CMIG_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_2003_CMIG_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>, Lee Z, Boll DT, Duerk JL, Lewin JS, Wilson DL. Image registration and fusion for interventional MRI-guided thermal ablation of the prostate cancer. The Sixth Annual International Conference on Medical Imaging Computing &amp; Computer Assisted Intervention. Lecture Notes in Computer Science (LNCS) 2003;2879:364-372, Springer-Verlag Berlin Heidelberg.<\/p>\n \r\n\r\n\r\n\r\n\r\nFei_2003_MICCAI\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\/2003\/01\/Fei_2003_LNCS_Registration_fusion_interventional_MRI.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_2003_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\/2019\/09\/Fei_2003_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><strong>Fei BW<\/strong>, Lee Z, Duerk JL, Wilson DL. Image registration for interventional MRI-guided procedures: similarity measurements, interpolation methods, and applications to the prostate. The Second International Workshop on Biomedical Image Registration, Philadelphia, PA, June 23-24, 2003, Lecture Notes in Computer Science (LNCS) 2003; 2717:321-329, Springer-Verlag Berlin Heidelberg.<\/p>\n \r\n\r\n\r\n\r\n\r\nFei_2003_IWBIR\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\/2003\/01\/Fei_2003_LNCS_Registration_fusion_interventional_MRI_2.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_2003_IWBIR_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_2003_IWBIR_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>, Frinkley K, Wilson DL. Registration algorithms for interventional MRI-guided treatment of the prostate. SPIE Medical Imaging: Visualization, Display, and Image-Guided Procedures, Edited by Robert L. Galloway, Proceedings of SPIE \u2013 The International Society for Optical Engineering 2003;5029:192-201.<\/p>\n \r\n\r\n\r\n\r\n\r\nFei_2003_MedImaging\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\/2003\/01\/Fei_2003_SPIE_Image_Interpolation_registration_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\/Fei_2003_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\/Fei_2003_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><strong>Fei BW<\/strong>, Duerk JL, Wilson DL. Automatic 3D registration for interventional MRI-guided treatment of prostate cancer. Computer Aided Surgery 2002; 7:257-267.<\/p>\n \r\n\r\n\r\n\r\n\r\nFei_2002_CAS\r\n\r\n\t\r\n\r\n<span class=\"pdf\">[<a target=\"_blank\" href=\"https:\/\/www.ncbi.nlm.nih.gov\/pubmed\/32528216\" 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_2002_CAS_Slice_volume_MRI_registration_prostate.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_2002_CAS_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_2002_CAS_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>, Boll DT, Duerk JL, Wilson DL. Image registration for interventional MRI-guided minimally invasive treatment of prostate cancer. The Second Joint Conference of the Annual Fall Meeting of the Biomedical Engineering Society and the IEEE EMBS\/BMES Conference, Proceedings of IEEE 2002; 2:1185.<\/p>\n \r\n\r\n\r\n\r\n\r\nFei_2002_BMES\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\/2002\/01\/Fei_2000_IEEE_EMBS_MRI_guided_Interventions.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_2002_BMES_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_2002_BMES_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>, Wheaton A, Lee Z, Nagano K, Duerk JL, Wilson DL. Robust registration method for interventional MRI-guided thermal ablation of prostate cancer. SPIE Medical Imaging 2001: Visualization, Display, and Image-Guided Procedures, Edited by Seong Kim Mum, Proceedings of SPIE \u2013 The International Society for Optical Engineering 2001;4319:53-60.<\/p>\n \r\n\r\n\r\n\r\n\r\nFei_2001_MedImaging\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\/2001\/01\/Fei_2001_SPIE_Slice_to_volume_registration_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\/Fei_2001_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\/Fei_2001_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><strong>Fei BW<\/strong>, Zhuang T, Hu J, Zhou F. Frameless stereotactic localization and multimodal image registration using DSA\/CT\/MRI. The 20thAnnual International Conference of the IEEE Engineering in Medicine and Biology Society, Proceedings of IEEE 1998;2:683-685.<\/p>\n \r\n\r\n\r\n\r\n\r\nFei_1998_IEEE\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\/09\/Fei_1998_IEEE_Frameless_stereotactic_localization.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_1998_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_1998_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>, Wang B, Bian Z, Cheng J. Numerical coding of phased-array in intelligent phased-array ultrasonic tomography. Journal of Biomedical Engineering 1993; 10:336-340.<\/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":4329,"comment_status":"closed","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[28,2],"tags":[],"class_list":["post-148","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>Clinical Imaging &#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\/clinical-imaging\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Clinical Imaging &#8226; 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