Understanding the architecture of mammalian brain at single-cell resolution is one of the key iss... more Understanding the architecture of mammalian brain at single-cell resolution is one of the key issues of neuroscience. However, mapping neuronal soma and projections throughout the whole brain is still challenging for imaging and data management technologies. Indeed, macroscopic volumes need to be reconstructed with high resolution and contrast in a reasonable time, producing datasets in the TeraByte range. We recently demonstrated an optical method (confocal light sheet microscopy, CLSM) capable of obtaining micron-scale reconstruction of entire mouse brains labeled with enhanced green fluorescent protein (EGFP). Combining light sheet illumination and confocal detection, CLSM allows deep imaging inside macroscopic cleared specimens with high contrast and speed. Here we describe the complete experimental pipeline to obtain comprehensive and human-readable images of entire mouse brains labeled with fluorescent proteins. The clearing and the mounting procedures are described, together with the steps to perform an optical tomography on its whole volume by acquiring many parallel adjacent stacks. We showed the usage of open-source custom-made software tools enabling stitching of the multiple stacks and multi-resolution data navigation. Finally, we illustrated some example of brain maps: the cerebellum from an L7-GFP transgenic mouse, in which all Purkinje cells are selectively labeled, and the whole brain from a thy1-GFP-M mouse, characterized by a random sparse neuronal labeling.
The evolution of the Fast Messages (FM) communication library has shown the importance of ow cont... more The evolution of the Fast Messages (FM) communication library has shown the importance of ow control to deliver the raw hardware performance of currently available interconnection networks to the applications. However, the credit-based solution used in FM to provide ow control, though simple and e cient for systems of limited dimension, does not scale well when the dimension of the parallel system increases. In this paper, we propose an extension to the FM ow control algorithm where credits can be assigned on-demand to communicating nodes. The experimental results reported in the paper demonstrate that the performance of this new scheme is virtually insensitive to system dimension providing that messages are long enough and there are not communication hot spots in the system. In presence of more general communication patterns, the proposed dynamic credit assignment mechanism can be used to implement adaptive credit allocation policies.
ABSTRACT Antinuclear autoantibodies (ANAs) are important markers to diagnose autoimmune diseases,... more ABSTRACT Antinuclear autoantibodies (ANAs) are important markers to diagnose autoimmune diseases, very serious and also invalidating illnesses. The benchmark procedure for ANAs diagnosis is the indirect immunofluorescence (IIF) assay performed on the HEp-2 substrate. Medical doctors first determine the fluorescence intensity exhibited by HEp-2 cells, and then report the staining pattern for positive wells only. With reference to staining pattern recognition, in the literature we found works recognizing five main patterns characterized by well-defined cell edges. These approaches are based on cell segmentation, a task that should be harder than the classification itself. We present here a method extending the panel of detectable HEp-2 staining patterns, introducing the centromere and cytoplasmic patterns, which do not show well-defined cell edges, and where a segmentation-based classification may fail. We apply a local approach which extracts SIFT descriptors and then classifies an image through the bag of visual words approach. This permits to represent complex image contents without applying the segmentation procedure. We test our approach on a dataset of HEp-2 images with large variability in both fluorescence intensity and staining patterns. Despite the large skew of the a-priori class distribution, our system correctly recognizes the 98.3% of samples, with a F-measure equal to 92.3%, 95.2% and 99.0%, for each class.
Communications in Computer and Information Science, 2010
... of Palombara Sabina (www.dasitaly.com), the comments of Dario Malosti, as well as Antonella A... more ... of Palombara Sabina (www.dasitaly.com), the comments of Dario Malosti, as well as Antonella Afeltra, Leonardo Onofri and Amelia Rigon for ... Rheumatology 46, 12901296 (2007) 4. Bizzaro, N., Tozzoli, R., Tonutti, E., Piazza, A., Manoni, F., Ghirardello, A., Bassetti, D., Villalta, D ...
19th IEEE Symposium on Computer-Based Medical Systems (CBMS'06), 2006
Indirect immunofluorescence is the recommended method for antinuclear autoantibodies (ANA) detect... more Indirect immunofluorescence is the recommended method for antinuclear autoantibodies (ANA) detection. IIF diagnosis requires estimating fluorescent intensity and pattern description, but resources and adequately trained personnel are not always available for these tasks. In this respect, an evident medical demand is the development of computer aided diagnosis tools that can offer a support to physician decision. In this paper we
At the present, Indirect Immunofluorescence (IIF) is the recommended method for the detection of ... more At the present, Indirect Immunofluorescence (IIF) is the recommended method for the detection of antinuclear autoantibodies (ANA). IIF diagnosis requires both the estimation of the fluorescent intensity and the description of the staining pattern, but resources and adequately trained personnel are not always available for these tasks. In this respect, an evident medical demand is the development of computer-aided diagnosis (CAD) tools that can offer a support to physician decision. In this paper we first propose a strategy to reliably label the image data set by using the diagnoses performed by different physicians, and then we present a system to classify the fluorescent intensity. Such a system adopts a multiple expert system architecture (MES), based on the classifier selection paradigm. Two different selection rules are presented and, given the application domain, the convenience of using one of them is analyzed. Different sets of operating points are determined, making the recognition system suited to application in daily practice and in a wide spectrum of scenarios. The measured performance on an annotated database of IIF images shows a low overall miss rate (\1.5%, 0.00% of false negative).
open-source 3D Visualization-assisted analysis (Vaa3D) is a software platform for the visualizati... more open-source 3D Visualization-assisted analysis (Vaa3D) is a software platform for the visualization and analysis of large-scale multidimensional images. In this protocol we describe how to use several popular features of Vaa3D, including (i) multidimensional image visualization, (ii) 3D image object generation and quantitative measurement, (iii) 3D image comparison, fusion and management, (iv) visualization of heterogeneous images and respective surface objects and (v) extension of Vaa3D functions using its plug-in interface. We also briefly demonstrate how to integrate these functions for complicated applications of microscopic image visualization and quantitative analysis using three exemplar pipelines, including an automated pipeline for image filtering, segmentation and surface generation; an automated pipeline for 3D image stitching; and an automated pipeline for neuron morphology reconstruction, quantification and comparison. once a user is familiar with Vaa3D, visualization usually runs in real time and analysis takes less than a few minutes for a simple data set.
This paper presents results of the performance tests of the PVM system over an ArM Metropolitan A... more This paper presents results of the performance tests of the PVM system over an ArM Metropolitan Area Network (MAN). PVM (Parallel Virtual Machine) is a communication package, which is considered currently a de facto standard for the development of high-performance distributed applications. ATM (Asynchronous Transfer Mode) is the emerging cell-switching technology for high-speed networks. This work is part of the experiments of heterogeneous network computing in the framework of the Neanet Project. Neanet is one of the first public high-speed networks based on ATM. The ultimate goal of the e.iperlments is to assess the effectiveness of ATM geographical networks for high-performance computing. Besides allowing comparison with local area networks, the YVM performance figures are useful per se in the design and in the predictive performance evaluation of distributed applications on ATM networks.
In this paper, we report our experience in looking for effective autofocus functions for automati... more In this paper, we report our experience in looking for effective autofocus functions for automatic image acquisition in ImmunoFluorescence Assay (IFA). We propose to use two functions that greatly improve the performance with respect to functions commonly proposed in the literature. The first function is based on the image histogram and it is suited to in the coarse phase of
ABSTRACT Higher-order logic languages seem particularly suitable to be implemented on parallelarc... more ABSTRACT Higher-order logic languages seem particularly suitable to be implemented on parallelarchitectures for at lest two reasons. On one hand, they offer more opportunitiesfor parallelization than first-order logic languages. On the other hand, the greatercomputational costs of higher-order unification of typed -terms should allow the exploitationof parallelism with a larger granularity, possibly leading to higher efficiency.In spite of their potential interest, there are a few...
13th IEEE International Conference on BioInformatics and BioEngineering, 2013
The analysis of EEG evoked potentials strongly relies on the correct alignment of different segme... more The analysis of EEG evoked potentials strongly relies on the correct alignment of different segments of the recorded EEG activity. The alignment of segments is needed in order to extract event related waves hidden in the background EEG activity. Commonly, the information on onsets is provided by the acquisition system. However, wrong configuration of the recording system or human errors during the acquisition or storage of data may make this information unavailable. Usually, these errors are discovered during the datasets analysis stage, and this stage can take place even several months after the acquisition of datasets. Unluckily, changes on patients status and the expensiveness of EEG registrations make unfeasible to repeat the acquisitions. In this paper we present and evaluate two mechanisms that we included in an EEGLAB plugin for the automatic reconstruction of onsets in EEG-TMS recordings. The methods of the TMS Triggers Reconstruction Software (TRS-TMS) plugin are discussed and evaluated obtaining guidelines for their correct configuration in the routine usage.
Indirect Immunofluorescence on HEp-2 slides is the recommended technique to detect antinuclear au... more Indirect Immunofluorescence on HEp-2 slides is the recommended technique to detect antinuclear autoantibodies in patient serum. Such slides are read at the fluorescence microscope by experts of IIF, who classify the fluorescence intensity, recognize mitotic cells and classify the staining patterns for each well. The crucial need of accurately performed and correctly reported laboratory determinations has motivated recent research on computer-aided diagnosis tools in IIF to support the HEp-2 image classification. Such systems adopt a fully supervised classification approach and, hence, their chance of success depends on the quality of ground truth used to train the classification algorithms. Besides being expensive and time consuming, collecting a large and reliable ground truth in IIF is intrinsically hard due to the inter-and intra-observer variability. In order to overcome such limitations, this paper presents a slightly supervised approach for positive/negative fluorescence intensity classification. The classification phase consists in matching parts of interest automatically detected in the test image with a Gaussian mixture model built over few control images. The approach, whose operating configuration can be adapted to the cost of misclassifications, has been tested over a database with 914 images acquired from 304 different wells, achieving remarkable results on positive/negative screening task.
Large deployments of access points in wireless local area networks (WLANs) based on the IEEE 802.... more Large deployments of access points in wireless local area networks (WLANs) based on the IEEE 802.11 standard require management, configuration and control mechanisms. Centralized WLANs are defined as multi-cell wireless access networks that implement some of these functions in a centralized manner. In this chapter the authors illustrate how the mechanisms designed for the management of centralized WLANs can also be used for monitoring parameters related to QoS support and for pursuing QoS goals. They describe the Control and Provisioning Wireless Access Protocol (CAPWAP), a recent IETF standard for the management of centralized WLANs which is currently in the final stages of the definition process, its implementation for the existing types of centralized WLANs, and its use for monitoring and QoS management. The authors discuss the QoS goals that can be pursued in this framework, such as access control, load balancing, cell resizing, and Medium Access Control parameters adaptation, a...
2010 20th International Conference on Pattern Recognition, 2010
Decomposition methods are multiclass classification schemes where the polychotomy is reduced into... more Decomposition methods are multiclass classification schemes where the polychotomy is reduced into several dichotomies. Each dichotomy is addressed by a classifier trained on a training set derived from the original one on the basis of the decomposition rule adopted. These new training sets may present a disproportion between the classes, harming the global recognition accuracy. Indeed, traditional learning algorithms are
Understanding the architecture of mammalian brain at single-cell resolution is one of the key iss... more Understanding the architecture of mammalian brain at single-cell resolution is one of the key issues of neuroscience. However, mapping neuronal soma and projections throughout the whole brain is still challenging for imaging and data management technologies. Indeed, macroscopic volumes need to be reconstructed with high resolution and contrast in a reasonable time, producing datasets in the TeraByte range. We recently demonstrated an optical method (confocal light sheet microscopy, CLSM) capable of obtaining micron-scale reconstruction of entire mouse brains labeled with enhanced green fluorescent protein (EGFP). Combining light sheet illumination and confocal detection, CLSM allows deep imaging inside macroscopic cleared specimens with high contrast and speed. Here we describe the complete experimental pipeline to obtain comprehensive and human-readable images of entire mouse brains labeled with fluorescent proteins. The clearing and the mounting procedures are described, together with the steps to perform an optical tomography on its whole volume by acquiring many parallel adjacent stacks. We showed the usage of open-source custom-made software tools enabling stitching of the multiple stacks and multi-resolution data navigation. Finally, we illustrated some example of brain maps: the cerebellum from an L7-GFP transgenic mouse, in which all Purkinje cells are selectively labeled, and the whole brain from a thy1-GFP-M mouse, characterized by a random sparse neuronal labeling.
The evolution of the Fast Messages (FM) communication library has shown the importance of ow cont... more The evolution of the Fast Messages (FM) communication library has shown the importance of ow control to deliver the raw hardware performance of currently available interconnection networks to the applications. However, the credit-based solution used in FM to provide ow control, though simple and e cient for systems of limited dimension, does not scale well when the dimension of the parallel system increases. In this paper, we propose an extension to the FM ow control algorithm where credits can be assigned on-demand to communicating nodes. The experimental results reported in the paper demonstrate that the performance of this new scheme is virtually insensitive to system dimension providing that messages are long enough and there are not communication hot spots in the system. In presence of more general communication patterns, the proposed dynamic credit assignment mechanism can be used to implement adaptive credit allocation policies.
ABSTRACT Antinuclear autoantibodies (ANAs) are important markers to diagnose autoimmune diseases,... more ABSTRACT Antinuclear autoantibodies (ANAs) are important markers to diagnose autoimmune diseases, very serious and also invalidating illnesses. The benchmark procedure for ANAs diagnosis is the indirect immunofluorescence (IIF) assay performed on the HEp-2 substrate. Medical doctors first determine the fluorescence intensity exhibited by HEp-2 cells, and then report the staining pattern for positive wells only. With reference to staining pattern recognition, in the literature we found works recognizing five main patterns characterized by well-defined cell edges. These approaches are based on cell segmentation, a task that should be harder than the classification itself. We present here a method extending the panel of detectable HEp-2 staining patterns, introducing the centromere and cytoplasmic patterns, which do not show well-defined cell edges, and where a segmentation-based classification may fail. We apply a local approach which extracts SIFT descriptors and then classifies an image through the bag of visual words approach. This permits to represent complex image contents without applying the segmentation procedure. We test our approach on a dataset of HEp-2 images with large variability in both fluorescence intensity and staining patterns. Despite the large skew of the a-priori class distribution, our system correctly recognizes the 98.3% of samples, with a F-measure equal to 92.3%, 95.2% and 99.0%, for each class.
Communications in Computer and Information Science, 2010
... of Palombara Sabina (www.dasitaly.com), the comments of Dario Malosti, as well as Antonella A... more ... of Palombara Sabina (www.dasitaly.com), the comments of Dario Malosti, as well as Antonella Afeltra, Leonardo Onofri and Amelia Rigon for ... Rheumatology 46, 12901296 (2007) 4. Bizzaro, N., Tozzoli, R., Tonutti, E., Piazza, A., Manoni, F., Ghirardello, A., Bassetti, D., Villalta, D ...
19th IEEE Symposium on Computer-Based Medical Systems (CBMS'06), 2006
Indirect immunofluorescence is the recommended method for antinuclear autoantibodies (ANA) detect... more Indirect immunofluorescence is the recommended method for antinuclear autoantibodies (ANA) detection. IIF diagnosis requires estimating fluorescent intensity and pattern description, but resources and adequately trained personnel are not always available for these tasks. In this respect, an evident medical demand is the development of computer aided diagnosis tools that can offer a support to physician decision. In this paper we
At the present, Indirect Immunofluorescence (IIF) is the recommended method for the detection of ... more At the present, Indirect Immunofluorescence (IIF) is the recommended method for the detection of antinuclear autoantibodies (ANA). IIF diagnosis requires both the estimation of the fluorescent intensity and the description of the staining pattern, but resources and adequately trained personnel are not always available for these tasks. In this respect, an evident medical demand is the development of computer-aided diagnosis (CAD) tools that can offer a support to physician decision. In this paper we first propose a strategy to reliably label the image data set by using the diagnoses performed by different physicians, and then we present a system to classify the fluorescent intensity. Such a system adopts a multiple expert system architecture (MES), based on the classifier selection paradigm. Two different selection rules are presented and, given the application domain, the convenience of using one of them is analyzed. Different sets of operating points are determined, making the recognition system suited to application in daily practice and in a wide spectrum of scenarios. The measured performance on an annotated database of IIF images shows a low overall miss rate (\1.5%, 0.00% of false negative).
open-source 3D Visualization-assisted analysis (Vaa3D) is a software platform for the visualizati... more open-source 3D Visualization-assisted analysis (Vaa3D) is a software platform for the visualization and analysis of large-scale multidimensional images. In this protocol we describe how to use several popular features of Vaa3D, including (i) multidimensional image visualization, (ii) 3D image object generation and quantitative measurement, (iii) 3D image comparison, fusion and management, (iv) visualization of heterogeneous images and respective surface objects and (v) extension of Vaa3D functions using its plug-in interface. We also briefly demonstrate how to integrate these functions for complicated applications of microscopic image visualization and quantitative analysis using three exemplar pipelines, including an automated pipeline for image filtering, segmentation and surface generation; an automated pipeline for 3D image stitching; and an automated pipeline for neuron morphology reconstruction, quantification and comparison. once a user is familiar with Vaa3D, visualization usually runs in real time and analysis takes less than a few minutes for a simple data set.
This paper presents results of the performance tests of the PVM system over an ArM Metropolitan A... more This paper presents results of the performance tests of the PVM system over an ArM Metropolitan Area Network (MAN). PVM (Parallel Virtual Machine) is a communication package, which is considered currently a de facto standard for the development of high-performance distributed applications. ATM (Asynchronous Transfer Mode) is the emerging cell-switching technology for high-speed networks. This work is part of the experiments of heterogeneous network computing in the framework of the Neanet Project. Neanet is one of the first public high-speed networks based on ATM. The ultimate goal of the e.iperlments is to assess the effectiveness of ATM geographical networks for high-performance computing. Besides allowing comparison with local area networks, the YVM performance figures are useful per se in the design and in the predictive performance evaluation of distributed applications on ATM networks.
In this paper, we report our experience in looking for effective autofocus functions for automati... more In this paper, we report our experience in looking for effective autofocus functions for automatic image acquisition in ImmunoFluorescence Assay (IFA). We propose to use two functions that greatly improve the performance with respect to functions commonly proposed in the literature. The first function is based on the image histogram and it is suited to in the coarse phase of
ABSTRACT Higher-order logic languages seem particularly suitable to be implemented on parallelarc... more ABSTRACT Higher-order logic languages seem particularly suitable to be implemented on parallelarchitectures for at lest two reasons. On one hand, they offer more opportunitiesfor parallelization than first-order logic languages. On the other hand, the greatercomputational costs of higher-order unification of typed -terms should allow the exploitationof parallelism with a larger granularity, possibly leading to higher efficiency.In spite of their potential interest, there are a few...
13th IEEE International Conference on BioInformatics and BioEngineering, 2013
The analysis of EEG evoked potentials strongly relies on the correct alignment of different segme... more The analysis of EEG evoked potentials strongly relies on the correct alignment of different segments of the recorded EEG activity. The alignment of segments is needed in order to extract event related waves hidden in the background EEG activity. Commonly, the information on onsets is provided by the acquisition system. However, wrong configuration of the recording system or human errors during the acquisition or storage of data may make this information unavailable. Usually, these errors are discovered during the datasets analysis stage, and this stage can take place even several months after the acquisition of datasets. Unluckily, changes on patients status and the expensiveness of EEG registrations make unfeasible to repeat the acquisitions. In this paper we present and evaluate two mechanisms that we included in an EEGLAB plugin for the automatic reconstruction of onsets in EEG-TMS recordings. The methods of the TMS Triggers Reconstruction Software (TRS-TMS) plugin are discussed and evaluated obtaining guidelines for their correct configuration in the routine usage.
Indirect Immunofluorescence on HEp-2 slides is the recommended technique to detect antinuclear au... more Indirect Immunofluorescence on HEp-2 slides is the recommended technique to detect antinuclear autoantibodies in patient serum. Such slides are read at the fluorescence microscope by experts of IIF, who classify the fluorescence intensity, recognize mitotic cells and classify the staining patterns for each well. The crucial need of accurately performed and correctly reported laboratory determinations has motivated recent research on computer-aided diagnosis tools in IIF to support the HEp-2 image classification. Such systems adopt a fully supervised classification approach and, hence, their chance of success depends on the quality of ground truth used to train the classification algorithms. Besides being expensive and time consuming, collecting a large and reliable ground truth in IIF is intrinsically hard due to the inter-and intra-observer variability. In order to overcome such limitations, this paper presents a slightly supervised approach for positive/negative fluorescence intensity classification. The classification phase consists in matching parts of interest automatically detected in the test image with a Gaussian mixture model built over few control images. The approach, whose operating configuration can be adapted to the cost of misclassifications, has been tested over a database with 914 images acquired from 304 different wells, achieving remarkable results on positive/negative screening task.
Large deployments of access points in wireless local area networks (WLANs) based on the IEEE 802.... more Large deployments of access points in wireless local area networks (WLANs) based on the IEEE 802.11 standard require management, configuration and control mechanisms. Centralized WLANs are defined as multi-cell wireless access networks that implement some of these functions in a centralized manner. In this chapter the authors illustrate how the mechanisms designed for the management of centralized WLANs can also be used for monitoring parameters related to QoS support and for pursuing QoS goals. They describe the Control and Provisioning Wireless Access Protocol (CAPWAP), a recent IETF standard for the management of centralized WLANs which is currently in the final stages of the definition process, its implementation for the existing types of centralized WLANs, and its use for monitoring and QoS management. The authors discuss the QoS goals that can be pursued in this framework, such as access control, load balancing, cell resizing, and Medium Access Control parameters adaptation, a...
2010 20th International Conference on Pattern Recognition, 2010
Decomposition methods are multiclass classification schemes where the polychotomy is reduced into... more Decomposition methods are multiclass classification schemes where the polychotomy is reduced into several dichotomies. Each dichotomy is addressed by a classifier trained on a training set derived from the original one on the basis of the decomposition rule adopted. These new training sets may present a disproportion between the classes, harming the global recognition accuracy. Indeed, traditional learning algorithms are
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Papers by G. Iannello