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1. giRAff: an automated atlas segmentation tool adapted to single histological slices

Author

Sébastien Piluso, Nicolas Souedet, Caroline Jan, Anne-Sophie Hérard, Cédric Clouchoux, and Thierry Delzescaux

Subjects
atlas segmentation, image registration, histology, brain, mouse, Neurosciences. Biological psychiatry. Neuropsychiatry, RC321-571
Abstract

Conventional histology of the brain remains the gold standard in the analysis of animal models. In most biological studies, standard protocols usually involve producing a limited number of histological slices to be analyzed. These slices are often selected into a specific anatomical region of interest or around a specific pathological lesion. Due to the lack of automated solutions to analyze such single slices, neurobiologists perform the segmentation of anatomical regions manually most of the time. Because the task is long, tedious, and operator-dependent, we propose an automated atlas segmentation method called giRAff, which combines rigid and affine registrations and is suitable for conventional histological protocols involving any number of single slices from a given mouse brain. In particular, the method has been tested on several routine experimental protocols involving different anatomical regions of different sizes and for several brains. For a given set of single slices, the method can automatically identify the corresponding slices in the mouse Allen atlas template with good accuracy and segmentations comparable to those of an expert. This versatile and generic method allows the segmentation of any single slice without additional anatomical context in about 1 min. Basically, our proposed giRAff method is an easy-to-use, rapid, and automated atlas segmentation tool compliant with a wide variety of standard histological protocols.

Published
2024
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2. A validation dataset for Macaque brain MRI segmentation

Author

Yaël Balbastre, Denis Rivière, Nicolas Souedet, Clara Fischer, Anne-Sophie Hérard, Susannah Williams, Michel E. Vandenberghe, Julien Flament, Romina Aron-Badin, Philippe Hantraye, Jean-François Mangin, and Thierry Delzescaux

Subjects
Computer applications to medicine. Medical informatics, R858-859.7, Science (General), Q1-390
Abstract

Validation data for segmentation algorithms dedicated to preclinical images is fiercely lacking, especially when compared to the large number of databases of Human brain images and segmentations available to the academic community. Not only is such data essential for validating methods, it is also needed for objectively comparing concurrent algorithms and detect promising paths, as segmentation challenges have shown for clinical images.The dataset we present here is a first step in this direction. It comprises 10 T2-weighted MRIs of healthy adult macaque brains, acquired on a 7 T magnet, along with corresponding manual segmentations into 17 brain anatomic labelled regions spread over 5 hierarchical levels based on a previously published macaque atlas (Calabrese et al., 2015) [1].By giving access to this unique dataset, we hope to provide a reference needed by the non-human primate imaging community. This dataset was used in an article presenting a new primate brain morphology analysis pipeline, Primatologist (Balbastre et al., 2017) [2]. Data is available through a NITRC repository (https://www.nitrc.org/projects/mircen_macset).

Published
2018
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3. Automated Individualization of Size-Varying and Touching Neurons in Macaque Cerebral Microscopic Images

Author

Zhenzhen You, Yaël Balbastre, Clément Bouvier, Anne-Sophie Hérard, Pauline Gipchtein, Philippe Hantraye, Caroline Jan, Nicolas Souedet, and Thierry Delzescaux

Subjects
neuron individualization, touching neurons, size-varying neurons, microscopic images, macaque brain, Neurosciences. Biological psychiatry. Neuropsychiatry, RC321-571, Human anatomy, QM1-695
Abstract

In biomedical research, cell analysis is important to assess physiological and pathophysiological information. Virtual microscopy offers the unique possibility to study the compositions of tissues at a cellular scale. However, images acquired at such high spatial resolution are massive, contain complex information, and are therefore difficult to analyze automatically. In this article, we address the problem of individualization of size-varying and touching neurons in optical microscopy two-dimensional (2-D) images. Our approach is based on a series of processing steps that incorporate increasingly more information. (1) After a step of segmentation of neuron class using a Random Forest classifier, a novel min-max filter is used to enhance neurons’ centroids and boundaries, enabling the use of region growing process based on a contour-based model to drive it to neuron boundary and achieve individualization of touching neurons. (2) Taking into account size-varying neurons, an adaptive multiscale procedure aiming at individualizing touching neurons is proposed. This protocol was evaluated in 17 major anatomical regions from three NeuN-stained macaque brain sections presenting diverse and comprehensive neuron densities. Qualitative and quantitative analyses demonstrate that the proposed method provides satisfactory results in most regions (e.g., caudate, cortex, subiculum, and putamen) and outperforms a baseline Watershed algorithm. Neuron counts obtained with our method show high correlation with an adapted stereology technique performed by two experts (respectively, 0.983 and 0.975 for the two experts). Neuron diameters obtained with our method ranged between 2 and 28.6 μm, matching values reported in the literature. Further works will aim to evaluate the impact of staining and interindividual variability on our protocol.

Published
2019
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4. ADAM30 Downregulates APP-Linked Defects Through Cathepsin D Activation in Alzheimer's Disease

Author

Florent Letronne, Geoffroy Laumet, Anne-Marie Ayral, Julien Chapuis, Florie Demiautte, Mathias Laga, Michel E. Vandenberghe, Nicolas Malmanche, Florence Leroux, Fanny Eysert, Yoann Sottejeau, Linda Chami, Amandine Flaig, Charlotte Bauer, Pierre Dourlen, Marie Lesaffre, Charlotte Delay, Ludovic Huot, Julie Dumont, Elisabeth Werkmeister, Franck Lafont, Tiago Mendes, Franck Hansmannel, Bart Dermaut, Benoit Deprez, Anne-Sophie Hérard, Marc Dhenain, Nicolas Souedet, Florence Pasquier, David Tulasne, Claudine Berr, Jean-Jacques Hauw, Yves Lemoine, Philippe Amouyel, David Mann, Rebecca Déprez, Frédéric Checler, David Hot, Thierry Delzescaux, Kris Gevaert, and Jean-Charles Lambert

Subjects
Alzheimer, APP, ADAM30, Amyloid, Metabolism, LTP, Medicine, Medicine (General), R5-920
Abstract

Although several ADAMs (A disintegrin-like and metalloproteases) have been shown to contribute to the amyloid precursor protein (APP) metabolism, the full spectrum of metalloproteases involved in this metabolism remains to be established. Transcriptomic analyses centred on metalloprotease genes unraveled a 50% decrease in ADAM30 expression that inversely correlates with amyloid load in Alzheimer's disease brains. Accordingly, in vitro down- or up-regulation of ADAM30 expression triggered an increase/decrease in Aβ peptides levels whereas expression of a biologically inactive ADAM30 (ADAM30mut) did not affect Aβ secretion. Proteomics/cell-based experiments showed that ADAM30-dependent regulation of APP metabolism required both cathepsin D (CTSD) activation and APP sorting to lysosomes. Accordingly, in Alzheimer-like transgenic mice, neuronal ADAM30 over-expression lowered Aβ42 secretion in neuron primary cultures, soluble Aβ42 and amyloid plaque load levels in the brain and concomitantly enhanced CTSD activity and finally rescued long term potentiation alterations. Our data thus indicate that lowering ADAM30 expression may favor Aβ production, thereby contributing to Alzheimer's disease development.

Published
2016
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5. Voxel-Based Statistical Analysis of 3D Immunostained Tissue Imaging

Author

Michel E. Vandenberghe, Nicolas Souedet, Anne-Sophie Hérard, Anne-Marie Ayral, Florent Letronne, Yaël Balbastre, Elmahdi Sadouni, Philippe Hantraye, Marc Dhenain, Frédérique Frouin, Jean-Charles Lambert, and Thierry Delzescaux

Subjects
voxel-based, 3D, immunohistochemistry, microscopy, rodent, Alzheimer's disease, Neurosciences. Biological psychiatry. Neuropsychiatry, RC321-571
Abstract

Recently developed techniques to visualize immunostained tissues in 3D and in large samples have expanded the scope of microscopic investigations at the level of the whole brain. Here, we propose to adapt voxel-based statistical analysis to 3D high-resolution images of the immunostained rodent brain. The proposed approach was first validated with a simulation dataset with known cluster locations. Then, it was applied to characterize the effect of ADAM30, a gene involved in the metabolism of the amyloid precursor protein, in a mouse model of Alzheimer's disease. This work introduces voxel-based analysis of 3D immunostained microscopic brain images and, therefore, opens the door to localized whole-brain exploratory investigation of pathological markers and cellular alterations.

Published
2018
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6. Welfare and research: automatic cognitive testing in social groups in macaques in the laboratory

Author

Christophe Jouy, Nicolas Souedet, Didier Thenadey, Philippe Hantraye, and Romina Aron Badin

Subjects
cognition, welfare, tactile screens, macaques, Science, Zoology, QL1-991
Abstract

Primate cognitive behavior in the laboratory has often been evaluated by housing subjects individually or isolating them and by imposing fluid or dietary restrictions to increase the subject’s motivation to work. Advances in animal welfare have significantly changed the way in which research institutions house primates in terms of space and numbers, accompanied by enrichment programs with novel objects and food that break with traditional feeding habits. Although some could potentially see these changes as a bias to previously published data, others have already proved that it is possible to obtain remarkable scientific results while offering primates a highly enriched environment. Inspired by recent publications on automated cognitive testing in social groups, our laboratory developed a special application on tactile screens, AUTOBUNTO, by which each primate learnt its own pin code to launch a single trial of its own behavioral test. This system allows testing animals on different cognitive tests while preserving social groups in their home cages. Two tactile screens can be installed at two ends of the gang cage to avoid dominance issues over screen availability. Results suggest that gang-training to touch tactile screens is quick and that completion of different cognitive tests can be acquired in a few weeks. More importantly, primates are free to work whenever they desire it instead of being imposed with a rigid testing schedule. Isolation or dietary restrictions seem unnecessary for primates to perform cognitive tests on tactile screens. Allowing access to two tactile screens is sufficient to avoid tension within the social group. In our experience, stereotypic behavior that can appear in primates housed individually or in small social groups, is absent in the presence of tactile screens, suggesting they represent a source of environmental enrichment for primates housed in the laboratory setting.

Published
2014
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17. Evaluation of automated segmentation algorithms for neurons in macaque cerebral microscopic images

Author

Anne-Sophie Hérard, Ming Jiang, Xiaojuan Ning, Cheng Shi, Zhenghao Shi, Thierry Delzescaux, Caroline Jan, Du Shuangli, Zhenzhen You, and Nicolas Souedet

Subjects
Histology, Jaccard index, Computer science, 02 engineering and technology, Macaque, Cross-validation, Machine Learning, 03 medical and health sciences, 0302 clinical medicine, biology.animal, Image Processing, Computer-Assisted, medicine, Animals, Segmentation, Instrumentation, Neurons, biology, business.industry, Deep learning, Putamen, Brain, 030206 dentistry, 021001 nanoscience & nanotechnology, Data set, Medical Laboratory Technology, medicine.anatomical_structure, nervous system, Macaca, Neuron, Artificial intelligence, Anatomy, 0210 nano-technology, business, Algorithm, Algorithms
Abstract

Accurate cerebral neuron segmentation is required before neuron counting and neuron morphological analysis. Numerous algorithms for neuron segmentation have been published, but they are mainly evaluated using limited subsets from a specific anatomical region, targeting neurons of clear contrast and/or neurons with similar staining intensity. It is thus unclear how these algorithms perform on cerebral neurons in diverse anatomical regions. In this article, we introduce and reliably evaluate existing machine learning algorithms using a data set of microscopy images of macaque brain. This data set highlights various anatomical regions (e.g., cortex, caudate, thalamus, claustrum, putamen, hippocampus, subiculum, lateral geniculate, globus pallidus, etc.), poor contrast, and staining intensity differences of neurons. The evaluation was performed using 10 architectures of six classic machine learning algorithms in terms of typical Recall, Precision, F-score, aggregated Jaccard index (AJI), as well as a performance ranking of algorithms. F-score of most of the algorithms is superior to 0.7. Deep learning algorithms facilitate generally higher F-scores. U-net with suitable layer depth has been evaluated to be excellent classifiers with F-score of 0.846 and 0.837 when performing cross validation. The evaluation and analysis indicate the performance gap among algorithms in various anatomical regions and the strengths and limitations of each algorithm. The comparative result highlights at the same time the importance and difficulty of neuron segmentation and provides clues for future improvement. To the best of our knowledge, this work is the first comprehensive study for neuron segmentation in such large-scale anatomical regions. Neuron segmentation plays a critical role in extracting cerebral information, such as neuron counting and neuron morphological analysis. Accurate automated cerebral neuron segmentation is a challenging task due to different kinds, poor contrast, staining intensity differences, and fuzzy boundaries of neurons. The comprehensive evaluation and analysis of performance among existing machine learning algorithms in diverse anatomical regions allows to make clear of the strengths and limitations of state-of-the-art algorithm. The comprehensive study provides clues for future improvement and creation of automated methods.

Published
2021
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18. Multiscale segmentation- and error-guided iterative convolutional neural network for cerebral neuron segmentation in microscopic images

Author

Zhenzhen You, Ming Jiang, Zhenghao Shi, Minghua Zhao, Cheng Shi, Shuangli Du, Anne‐Sophie Hérard, Nicolas Souedet, and Thierry Delzescaux

Subjects
Neurons, Medical Laboratory Technology, Histology, Image Processing, Computer-Assisted, Animals, Brain, Macaca, Neural Networks, Computer, Anatomy, Instrumentation
Abstract

This article uses microscopy images obtained from diverse anatomical regions of macaque brain for neuron semantic segmentation. The complex structure of brain, the large intra-class staining intensity difference within neuron class, the small inter-class staining intensity difference between neuron and tissue class, and the unbalanced dataset increase the difficulty of neuron semantic segmentation. To address this problem, we propose a multiscale segmentation- and error-guided iterative convolutional neural network (MSEG-iCNN) to improve the semantic segmentation performance in major anatomical regions of the macaque brain. After evaluating microscopic images from 17 anatomical regions, the semantic segmentation performance of neurons is improved by 10.6%, 4.0%, 1.5%, and 1.2% compared with Random Forest, FCN-8s, U-Net, and UNet++, respectively. Especially for neurons with brighter staining intensity in the anatomical regions such as lateral geniculate, globus pallidus and hypothalamus, the performance is improved by 66.1%, 23.9%, 11.2%, and 6.7%, respectively. Experiments show that our proposed method can efficiently segment neurons with a wide range of staining intensities. The semantic segmentation results are of great significance and can be further used for neuron instance segmentation, morphological analysis and disease diagnosis. Cell segmentation plays a critical role in extracting cerebral information, such as cell counting, cell morphometry and distribution analysis. Accurate automated neuron segmentation is challenging due to the complex structure of brain, the large intra-class staining intensity difference within neuron class, the small inter-class staining intensity difference between neuron and tissue class, and the unbalanced dataset. The proposed multiscale segmentation- and error-guided iterative convolutional neural network (MSEG-iCNN) improve the segmentation performance in 17 major anatomical regions of the macaque brain. HIGHLIGHTS: Cell segmentation plays a critical role in extracting cerebral information, such as cell counting, cell morphometry and distribution analysis. Accurate automated neuron segmentation is challenging due to the complex structure of brain, the large intra-class staining intensity difference within neuron class, the small inter-class staining intensity difference between neuron and tissue class, and the unbalanced dataset. The proposed multiscale segmentation- and error-guided iterative convolutional neural network (MSEG-iCNN) improve the segmentation performance in 17 major anatomical regions of the macaque brain.

Published
2022

19. A validation dataset for Macaque brain MRI segmentation

Author

Michel E. Vandenberghe, Yaël Balbastre, Clara Fischer, Susannah Williams, Julien Flament, Romina Aron-Badin, Philippe Hantraye, Nicolas Souedet, Jean-François Mangin, Anne-Sophie Hérard, Thierry Delzescaux, Denis Rivière, Laboratoire des Maladies Neurodégénératives - UMR 9199 (LMN), Centre National de la Recherche Scientifique (CNRS)-Service MIRCEN (MIRCEN), Université Paris-Saclay-Institut de Biologie François JACOB (JACOB), Direction de Recherche Fondamentale (CEA) (DRF (CEA)), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Direction de Recherche Fondamentale (CEA) (DRF (CEA)), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Université Paris-Saclay-Institut de Biologie François JACOB (JACOB), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA), Service NEUROSPIN (NEUROSPIN), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Université Paris-Saclay, Centre d'Acquisition et de Traitement des Images [Paris], Commissariat à l'énergie atomique et aux énergies alternatives (CEA), Service MIRCEN (MIRCEN), Institut d'Imagerie BioMédicale (I2BM), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Université Paris-Saclay, Unité Analyse et Traitement de l'Information (UNATI), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Université Paris-Saclay-Direction de Recherche Fondamentale (CEA) (DRF (CEA)), Université Paris-Sud - Paris 11 (UP11)-Service MIRCEN (MIRCEN), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Centre National de la Recherche Scientifique (CNRS), Institut de Recherches sur les Archéomatériaux (IRAMAT), Université de Technologie de Belfort-Montbeliard (UTBM)-Université d'Orléans (UO)-Université Bordeaux Montaigne-Centre National de la Recherche Scientifique (CNRS), Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-Institut de Biologie François JACOB (JACOB), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-Institut de Biologie François JACOB (JACOB), and Université Paris-Saclay-Direction de Recherche Fondamentale (CEA) (DRF (CEA))

Subjects
Computer science, [SDV.IB.IMA]Life Sciences [q-bio]/Bioengineering/Imaging, computer.software_genre, lcsh:Computer applications to medicine. Medical informatics, Macaque, 050105 experimental psychology, 03 medical and health sciences, 0302 clinical medicine, biology.animal, Brain mri, 0501 psychology and cognitive sciences, Segmentation, lcsh:Science (General), Multidisciplinary, biology, business.industry, 05 social sciences, Brain morphometry, Pattern recognition, Academic community, lcsh:R858-859.7, [SDV.NEU]Life Sciences [q-bio]/Neurons and Cognition [q-bio.NC], Artificial intelligence, Data mining, business, computer, 030217 neurology & neurosurgery, Neuroscience, lcsh:Q1-390
Abstract

International audience; Validation data for segmentation algorithms dedicated to preclinical images is fiercely lacking, especially when compared to the large number of databases of Human brain images and segmentations available to the academic community. Not only is such data essential for validating methods, it is also needed for objectively comparing concurrent algorithms and detect promising paths, as segmentation challenges have shown for clinical images.The dataset we present here is a first step in this direction. It comprises 10 T2-weighted MRIs of healthy adult macaque brains, acquired on a 7 T magnet, along with corresponding manual segmentations into 17 brain anatomic labelled regions spread over 5 hierarchical levels based on a previously published macaque atlas (Calabrese et al., 2015) [1].By giving access to this unique dataset, we hope to provide a reference needed by the non-human primate imaging community. This dataset was used in an article presenting a new primate brain morphology analysis pipeline, Primatologist (Balbastre et al., 2017) [2]. Data is available through a NITRC repository (https://www.nitrc.org/projects/mircen_macset).

Published
2018
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20. Quantitative evaluation of rigid and elastic registrations for abdominal perfusion imaging with X-ray computed tomography

Author

Muriel Lefort, Jean-Marc Tacchella, Thierry Delzescaux, Claire Pellot-Barakat, Olivier Lucidarme, A. De Cesare, J. Lebenberg, Mike-Ely Cohen, Frédérique Frouin, and Nicolas Souedet

Subjects
Rigid model, business.industry, Biomedical Engineering, Biophysics, Perfusion scanning, Tumor response, X ray computed, Medicine, Superimposition, Free-form deformation, Tomography, Dynamic ct, business, Nuclear medicine
Abstract

Objectives Dynamic X-ray computed tomography with contrast agent injection allows quantifying the tumor response to an antiangiogenic treatment. For hepatic tumors, it is crucial to correct for respiratory movements to ensure a good quantification of perfusion parameters. Material and Methods Two registration models were tested on eight dynamic exams: 1) rigid model and 2) elastic model based on Free Form Deformation. Each exam included about 50 volumes acquired under free-breathing conditions. The registration methods were quantitatively and automatically evaluated by defining a global score. This score was based on a Dice index and measured the superimposition rate between corresponding bony structures of different frames of the temporal exam. Results Before registration, the mean value of the score was 0.78. After rigid registration, these values were globally lower with an average of 0.73. After elastic registration, a mean score of 0.90 was reached which was much higher than before registration. Conclusion This automatic evaluation showed its interest for controlling the quality of dynamic CT registrations. It also helped proving that elastic methods are much more efficient than rigid methods for registering abdominal dynamic volumes, when considering the whole field of view.

Published
2013
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21. Primatologist: A modular segmentation pipeline for macaque brain morphometry

Author

Clara Fischer, Romina Aron-Badin, Thierry Delzescaux, Julien Flament, Philippe Hantraye, Yaël Balbastre, Denis Rivière, Susannah Williams, Anne-Sophie Hérard, Michel E. Vandenberghe, Nicolas Souedet, Jean-François Mangin, Laboratoire des Maladies Neurodégénératives - UMR 9199 (LMN), Service MIRCEN (MIRCEN), Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-Institut de Biologie François JACOB (JACOB), Direction de Recherche Fondamentale (CEA) (DRF (CEA)), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Direction de Recherche Fondamentale (CEA) (DRF (CEA)), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-Institut de Biologie François JACOB (JACOB), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Centre National de la Recherche Scientifique (CNRS), Service NEUROSPIN (NEUROSPIN), Université Paris-Saclay-Direction de Recherche Fondamentale (CEA) (DRF (CEA)), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA), Centre d'Acquisition et de Traitement des Images [Paris], Commissariat à l'énergie atomique et aux énergies alternatives (CEA), IRAMAT - Centre Ernest Babelon (IRAMAT-CEB), Institut de Recherches sur les Archéomatériaux (IRAMAT), Université de Technologie de Belfort-Montbeliard (UTBM)-Université d'Orléans (UO)-Université Bordeaux Montaigne (UBM)-Centre National de la Recherche Scientifique (CNRS)-Université de Technologie de Belfort-Montbeliard (UTBM)-Université d'Orléans (UO)-Université Bordeaux Montaigne (UBM)-Centre National de la Recherche Scientifique (CNRS), Institut d'Imagerie BioMédicale (I2BM), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Université Paris-Saclay, Unité Analyse et Traitement de l'Information (UNATI), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Université Paris-Saclay-Direction de Recherche Fondamentale (CEA) (DRF (CEA)), Université Paris-Sud - Paris 11 (UP11)-Service MIRCEN (MIRCEN), Université Paris-Saclay-Institut de Biologie François JACOB (JACOB), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Université Paris-Saclay-Institut de Biologie François JACOB (JACOB), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Université Paris-Saclay, Université de Technologie de Belfort-Montbeliard (UTBM)-Université d'Orléans (UO)-Université Bordeaux Montaigne-Centre National de la Recherche Scientifique (CNRS), Université d'Orléans (UO)-Centre National de la Recherche Scientifique (CNRS)-Université Bordeaux Montaigne-Université de Technologie de Belfort-Montbeliard (UTBM)-Université d'Orléans (UO)-Centre National de la Recherche Scientifique (CNRS)-Université Bordeaux Montaigne-Université de Technologie de Belfort-Montbeliard (UTBM), Institut de Biologie François JACOB (JACOB), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS), and Université de Technologie de Belfort-Montbeliard (UTBM)-Université d'Orléans (UO)-Université Bordeaux Montaigne-Centre National de la Recherche Scientifique (CNRS)-Université de Technologie de Belfort-Montbeliard (UTBM)-Université d'Orléans (UO)-Université Bordeaux Montaigne-Centre National de la Recherche Scientifique (CNRS)

Subjects
0301 basic medicine, Computer science, [SDV.IB.IMA]Life Sciences [q-bio]/Bioengineering/Imaging, Cognitive Neuroscience, Expectation-maximization, Initialization, Neuroimaging, Macaque, 03 medical and health sciences, Atlases as Topic, Imaging, Three-Dimensional, 0302 clinical medicine, Segmentation, biology.animal, Image Processing, Computer-Assisted, medicine, Animals, Computer vision, medicine.diagnostic_test, biology, business.industry, Brain morphometry, Brain, Magnetic resonance imaging, Pattern recognition, Magnetic Resonance Imaging, 030104 developmental biology, Neurology, Primatologist, Brain size, Macaca, [SDV.NEU]Life Sciences [q-bio]/Neurons and Cognition [q-bio.NC], Artificial intelligence, business, Algorithms, Software, 030217 neurology & neurosurgery, MRI
Abstract

International audience; Because they bridge the genetic gap between rodents and humans, non-human primates (NHPs) play a major role in therapy development and evaluation for neurological disorders. However, translational research success from NHPs to patients requires an accurate phenotyping of the models. In patients, magnetic resonance imaging (MRI) combined with automated segmentation methods has offered the unique opportunity to assess in vivo brain morphological changes. Meanwhile, specific challenges caused by brain size and high field contrasts make existing algorithms hard to use routinely in NHPs. To tackle this issue, we propose a complete pipeline, Prima-tologist, for multi-region segmentation. Tissue segmentation is based on a modular statistical model that includes random field regularization, bias correction and denoising and is optimized by expectation-maximization. To deal with the broad variety of structures with different relaxing times at 7 T, images are segmented into 17 anatomical classes, including subcortical regions. Pre-processing steps insure a good initialization of the parameters and thus the robustness of the pipeline. It is validated on 10 T2-weighted MRIs of healthy macaque brains. Classification scores are compared with those of a non-linear atlas registration, and the impact of each module on classification scores is thoroughly evaluated.

Published
2017
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22. Automated cell individualization and counting in cerebral microscopic images

Author

Yaël Balbastre, Michel E. Vandenberghe, Philippe Hantraye, Anne-Sophie Hérard, Caroline Jan, Thierry Delzescaux, Nicolas Souedet, Zhenzhen You, Laboratoire des Maladies Neurodégénératives - UMR 9199 (LMN), Service MIRCEN (MIRCEN), Institut de Biologie François JACOB (JACOB), Direction de Recherche Fondamentale (CEA) (DRF (CEA)), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Direction de Recherche Fondamentale (CEA) (DRF (CEA)), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Université Paris-Saclay-Institut de Biologie François JACOB (JACOB), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Université Paris-Saclay, Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-Institut de Biologie François JACOB (JACOB), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-Institut de Biologie François JACOB (JACOB), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Centre National de la Recherche Scientifique (CNRS), and Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)

Subjects
0301 basic medicine, Computer science, business.industry, segmentation, Gaussian filter, Image (mathematics), Cell individualization, Maxima and minima, 03 medical and health sciences, symbols.namesake, 030104 developmental biology, 0302 clinical medicine, Signal-to-noise ratio, nervous system, min-max cartography, [INFO.INFO-TI]Computer Science [cs]/Image Processing [eess.IV], symbols, neuron counting, Segmentation, Computer vision, [SDV.NEU]Life Sciences [q-bio]/Neurons and Cognition [q-bio.NC], Artificial intelligence, business, microscopic images, 030217 neurology & neurosurgery
Abstract

International audience; In biomedical research, cell counting is important to assess physiological and pathophysiological information. However, the automated analysis of microscopic images of tissues remains extremely challenging. We propose an automated processing protocol for proper segmentation of individual cells in microscopic images. A Gaussian filter is applied to improve signal to noise ratio (SNR) then an original minmax method is proposed to produce an image in which information describing both cell centers (minima) and boundaries are enhanced. Finally, a contour-based model initialized from minima in the min-max cartography is carried out to achieve cell individualization. This method is evaluated on a NeuN-stained macaque brain section in sub-regions presenting various levels of fraction of neuron surface occupation. Comparison with several methods of reference demonstrates that the performances of our method are superior. A first application to the segmentation of neurons in the hippocampus illustrates the ability of our approach to deal with massive and complex data.

Published
2016
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23. ADAM30 Downregulates APP-Linked Defects Through Cathepsin D Activation in Alzheimer's Disease

Author

Pierre Dourlen, Yves Lemoine, Amandine Flaig, Frédéric Checler, Anne Marie Ayral, Jean-Charles Lambert, Kris Gevaert, Mathias Laga, Fanny Eysert, Tiago Mendes, David M. A. Mann, Linda Chami, Anne Sophie Hérard, Thierry Delzescaux, Franck Lafont, Florie Demiautte, Franck Hansmannel, Benoit Deprez, Charlotte Delay, David Hot, Florence Leroux, Bart Dermaut, Philippe Amouyel, Ludovic Huot, Yoann Sottejeau, Florence Pasquier, Nicolas Souedet, Florent Letronne, Charlotte Bauer, Marc Dhenain, Marie Lesaffre, Geoffroy Laumet, Nicolas Malmanche, David Tulasne, Claudine Berr, Rebecca Deprez, Julie Dumont, Elisabeth Werkmeister, Michel E. Vandenberghe, Jean Jacques Hauw, Julien Chapuis, Epidémiologie des maladies chroniques : impact des interactions gène environnement sur la santé des populations, Institut Pasteur de Lille, Réseau International des Instituts Pasteur (RIIP)-Réseau International des Instituts Pasteur (RIIP)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Université de Lille, Droit et Santé, Facteurs de Risque et Déterminants Moléculaires des Maladies liées au Vieillissement - U 1167 (RID-AGE), Réseau International des Instituts Pasteur (RIIP)-Réseau International des Instituts Pasteur (RIIP)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Université de Lille-Centre Hospitalier Régional Universitaire [Lille] (CHRU Lille), Vlaams Instituut voor Biotechnologie [Ghent, Belgique] (VIB), Universiteit Gent = Ghent University (UGENT), Laboratoire des Maladies Neurodégénératives - UMR 9199 (LMN), Service MIRCEN (MIRCEN), Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-Institut de Biologie François JACOB (JACOB), Direction de Recherche Fondamentale (CEA) (DRF (CEA)), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Direction de Recherche Fondamentale (CEA) (DRF (CEA)), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-Institut de Biologie François JACOB (JACOB), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Centre National de la Recherche Scientifique (CNRS), Médicaments et molécules pour agir sur les Systèmes Vivants - U 1177 (M2SV), Réseau International des Instituts Pasteur (RIIP)-Réseau International des Instituts Pasteur (RIIP)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Université de Lille, Institut de pharmacologie moléculaire et cellulaire (IPMC), Université Nice Sophia Antipolis (1965 - 2019) (UNS), COMUE Université Côte d'Azur (2015-2019) (COMUE UCA)-COMUE Université Côte d'Azur (2015-2019) (COMUE UCA)-Centre National de la Recherche Scientifique (CNRS)-Université Côte d'Azur (UCA), Mécanismes de la Tumorigénèse et Thérapies Ciblées - UMR 8161 (M3T), Réseau International des Instituts Pasteur (RIIP)-Réseau International des Instituts Pasteur (RIIP)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Université de Lille-Centre National de la Recherche Scientifique (CNRS), Centre d’Infection et d’Immunité de Lille - INSERM U 1019 - UMR 9017 - UMR 8204 (CIIL), Réseau International des Instituts Pasteur (RIIP)-Réseau International des Instituts Pasteur (RIIP)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Université de Lille-Centre Hospitalier Régional Universitaire [Lille] (CHRU Lille)-Centre National de la Recherche Scientifique (CNRS), Réseau International des Instituts Pasteur (RIIP), Nutrition-Génétique et Exposition aux Risques Environnementaux (NGERE), Institut National de la Santé et de la Recherche Médicale (INSERM)-Université de Lorraine (UL), Service d'Hépato-gastro-entérologie [CHRU Nancy], Centre Hospitalier Régional Universitaire de Nancy (CHRU Nancy), CEA [Fontenay-aux-Roses] (UGRA / SETA), Commissariat à l'énergie atomique et aux énergies alternatives (CEA), Troubles cognitifs dégénératifs et vasculaires - U 1171 - EA 1046 (TCDV), Institut National de la Santé et de la Recherche Médicale (INSERM)-Université de Lille, Droit et Santé-Centre Hospitalier Régional Universitaire [Lille] (CHRU Lille), Neuropsychiatrie : recherche épidémiologique et clinique (PSNREC), Université Montpellier 1 (UM1)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Université de Montpellier (UM), Laboratoire de Neuropathologie Raymond Escourolle [CHU Pitié-Salpétriêre], Université Pierre et Marie Curie - Paris 6 (UPMC)-CHU Pitié-Salpêtrière [AP-HP], Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU), Institute of Brain, Behaviour and Mental Health, University of Manchester [Manchester], Hérard, Anne-Sophie, Université Paris-Saclay-Institut de Biologie François JACOB (JACOB), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Université Paris-Saclay-Institut de Biologie François JACOB (JACOB), Troubles cognitifs dégénératifs et vasculaires - U 1171 (TCDV), Institut National de la Santé et de la Recherche Médicale (INSERM)-Université de Lille-Centre Hospitalier Régional Universitaire [Lille] (CHRU Lille), Institut de Biologie François JACOB (JACOB), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS), CDithem Platform, IGM, CHU Pitié-Salpêtrière [AP-HP], Sorbonne Université (SU)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP), Centre National de la Recherche Scientifique (CNRS)-Université Nice Sophia Antipolis (... - 2019) (UNS), COMUE Université Côte d'Azur (2015-2019) (COMUE UCA)-COMUE Université Côte d'Azur (2015-2019) (COMUE UCA)-Université Côte d'Azur (UCA), Centre de Recherche Jean-Pierre AUBERT Neurosciences et Cancer - U1172 Inserm - U837 (JPArc), Institut National de la Santé et de la Recherche Médicale (INSERM)-Université Lille Nord de France (COMUE)-Université de Lille, Centre National de la Recherche Scientifique (CNRS)-Centre Hospitalier Régional Universitaire [Lille] (CHRU Lille)-Université de Lille-Institut National de la Santé et de la Recherche Médicale (INSERM)-Institut Pasteur de Lille, Réseau International des Instituts Pasteur (RIIP)-Réseau International des Instituts Pasteur (RIIP), Récepteurs nucléaires, maladies cardiovasculaires et diabète - U 1011 (RNMCD), Institut Fédératif de Recherche 142, Institut Fédératif de Recherche, Center for Medical Genetics [Ghent], Ghent University Hospital, Département de neurologie, Centre Hospitalier Régional Universitaire [Lille] (CHRU Lille), Institut de biologie de Lille - IBL (IBLI), Université de Lille, Sciences et Technologies-Institut Pasteur de Lille, Réseau International des Instituts Pasteur (RIIP)-Réseau International des Instituts Pasteur (RIIP)-Université de Lille, Droit et Santé-Centre National de la Recherche Scientifique (CNRS), Laboratoire de Neuropathologie Raymond Escourolle, Université Pierre et Marie Curie - Paris 6 (UPMC)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-CHU Pitié-Salpêtrière [AP-HP], Sorbonne Université (SU)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU), Division of Neuroscience and Experimental Psychology [Salford, UK], University of Manchester [Manchester]-Salford Royal NHS Foundation Trust [Salford, UK], Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Université Paris-Saclay, Dept Med Prot Res, Flanders Institute for Biotechnology, Département Imagerie et Simulation pour le Contrôle (DISC), Laboratoire d'Intégration des Systèmes et des Technologies (LIST), Direction de Recherche Technologique (CEA) (DRT (CEA)), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Direction de Recherche Technologique (CEA) (DRT (CEA)), Laboratoire des Maladies Neurodégénératives - UMR 9199 [LMN], Institut de pharmacologie moléculaire et cellulaire [IPMC], Centre de Recherche Jean-Pierre AUBERT Neurosciences et Cancer - U837 [JPArc], Centre d’Infection et d’Immunité de Lille - INSERM U 1019 - UMR 9017 - UMR 8204 [CIIL], Récepteurs nucléaires, maladies cardiovasculaires et diabète - U 1011 [RNMCD], Facteurs de Risque et Déterminants Moléculaires des Maladies liées au Vieillissement - U 1167 [RID-AGE], Nutrition-Génétique et Exposition aux Risques Environnementaux [NGERE], CEA [Fontenay-aux-Roses] [UGRA / SETA], and Institut de biologie de Lille - IBL [IBLI]

Subjects
0301 basic medicine, Patch-Clamp Techniques, ADAM, A Disintegrin and Metalloproteinase Domain, lcsh:Medicine, Cathepsin D, COFRADIC, combined fractional diagonal chromatography, ALPHA-SECRETASE, Mice, 0302 clinical medicine, Pepstatins, Amyloid precursor protein, BRAIN, RNA, Small Interfering, Tissues and Organs (q-bio.TO), GENE-EXPRESSION, lcsh:R5-920, ROLES, IRS4, insulin receptor substrate 4, P3 peptide, Brain, Long-term potentiation, General Medicine, AMYLOID PRECURSOR PROTEIN, Alpha secretase, BACE, Beta-site APP cleaving enzyme 1, Neurons and Cognition (q-bio.NC), [SDV.NEU]Life Sciences [q-bio]/Neurons and Cognition [q-bio.NC], RNA Interference, Macrolides, LTP, lcsh:Medicine (General), GKAP1, G kinase-anchoring protein 1, Research Paper, Genetically modified mouse, Amyloid, CTSD, cathepsin D, Down-Regulation, Mice, Transgenic, Biology, General Biochemistry, Genetics and Molecular Biology, 03 medical and health sciences, TD2, Type 2 diabetes, Alzheimer Disease, APP, amyloid precursor protein, Cell Line, Tumor, Animals, Humans, Secretion, [SDV.NEU] Life Sciences [q-bio]/Neurons and Cognition [q-bio.NC], Amino Acid Sequence, TRANSGENIC MOUSE MODEL, Amyloid beta-Peptides, PLA, proximity ligation assay, lcsh:R, LTP, long term potentiation, Biology and Life Sciences, Quantitative Biology - Tissues and Organs, ADAM30, DEGRADATION, CamKIIα, Ca2 +/calmodulin-dependent protein kinase II alpha, Molecular biology, Mice, Inbred C57BL, MICE, ADAM Proteins, 030104 developmental biology, HEK293 Cells, Metabolism, Microscopy, Fluorescence, Quantitative Biology - Neurons and Cognition, FOS: Biological sciences, COGNITIVE DECLINE, biology.protein, Alzheimer, BSA, bovine serum albumin, MMP, metalloproteinase, INHIBITORS, Lysosomes, APP, MRI, magnetic resonance imaging, 030217 neurology & neurosurgery
Abstract

Although several ADAMs (A disintegrin-like and metalloproteases) have been shown to contribute to the amyloid precursor protein (APP) metabolism, the full spectrum of metalloproteases involved in this metabolism remains to be established. Transcriptomic analyses centred on metalloprotease genes unraveled a 50% decrease in ADAM30 expression that inversely correlates with amyloid load in Alzheimer's disease brains. Accordingly, in vitro down- or up-regulation of ADAM30 expression triggered an increase/decrease in Aβ peptides levels whereas expression of a biologically inactive ADAM30 (ADAM30mut) did not affect Aβ secretion. Proteomics/cell-based experiments showed that ADAM30-dependent regulation of APP metabolism required both cathepsin D (CTSD) activation and APP sorting to lysosomes. Accordingly, in Alzheimer-like transgenic mice, neuronal ADAM30 over-expression lowered Aβ42 secretion in neuron primary cultures, soluble Aβ42 and amyloid plaque load levels in the brain and concomitantly enhanced CTSD activity and finally rescued long term potentiation alterations. Our data thus indicate that lowering ADAM30 expression may favor Aβ production, thereby contributing to Alzheimer's disease development., Highlights • By transcriptomic analyses, low levels of ADAM30 expression was observed in the brain of AD cases (compared with controls). • Activation of cathepsin D by ADAM30 is required to modulate the APP metabolism in vitro, i.e. decrease in Ab secretion. • In AD-like mice, neuronal ADAM30 expression led to lower soluble Aβ42, amyloid plaques and to rescue long term potentiation The amyloid precursor protein (APP) processing is central in the etiology of Alzheimer's disease (AD). Characterizing the actors of this metabolism is thus an important challenge. We searched for proteins involved in the APP processing and we selected the A disintegrin-like and metalloprotease 30 (ADAM30). Our results revealed an axis in APP metabolism pathway through an ADAM30-dependent cathepsin D activation. When activated following ADAM30 expression, a decrease in Aβ secretion occurred in vitro and in vivo. ADAM30 under-expression observed in AD brains and thus the impairment of the ADAM30-dependent pathway may favor Aβ peptide production and, ultimately, AD development.

Published
2016
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24. Registration and functional analysis of CT dynamic image sequences for the follow-up of patients with hepatic tumors undergoing antiangiogenic therapy

Author

B. Romain, Thierry Delzescaux, Sébastien Mulé, J. Dauguet, Olivier Lucidarme, A. Guibal, Frédérique Frouin, Nicolas Souedet, and Yasmina Chenoune

Subjects
Motion compensation, Liver tumor, Parametric Image, business.industry, media_common.quotation_subject, Biomedical Engineering, Biophysics, Image registration, Image processing, medicine.disease, Image (mathematics), Medicine, Contrast (vision), business, Nuclear medicine, Perfusion, media_common
Abstract

Objective A new approach for evaluating tumor response to antiangiogenic treatment using dynamic contrast-enhanced perfusion computed tomography (CT) was provided. Patients and methods Five patients, with hepatic tumors, were examined before and a few weeks after therapy. Following injection of a contrast agent, dynamic image acquisitions were obtained during two minutes, with eight axial sections by volume. To analyze these functional data, we proposed an image processing pipeline. We first applied a rigid registration, based on a blockmatching method, to correct for respiratory motion. We then calculated parametric image volumes, using adapted reference kinetics. These image volumes allowed us to differentiate between the various contrast enhancement kinetics (arterial/venous, healthy/pathological tissues), following the injection of contrast agent. Results The registration was validated qualitatively (by visual inspection of registered image volumes) and quantitatively (using criteria based on the estimation of a respiratory motion component). Parametric image volumes allowed us to differentiate healthy tissues from tumor tissues, and to display necrotic regions, which occurred in four patients, after therapy. Discussion The proposed approach allows us to compensate for respiratory motion in a region localized around the tumor and could be further extended by a nonrigid registration. Conclusion The robust computation of parametric image volumes enables a local and precise display of the tumor response to an antiangiogenic therapy.

Published
2010
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25. High-throughput 3D whole-brain quantitative histopathology in rodents

Author

Elmahdi Sadouni, Nicolas Souedet, Laurent Pradier, Anne-Sophie Hérard, Jocelyne Schulz, Michel E. Vandenberghe, Marc Dhenain, Mathieu Santin, Véronique Blanchard, Thomas Rooney, Thomas Debeir, Dominique Briet, Philippe Hantraye, Thierry Delzescaux, Denis Carré, P. E. Chabrier, Laboratoire des Maladies Neurodégénératives - UMR 9199 (LMN), Service MIRCEN (MIRCEN), Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-Institut de Biologie François JACOB (JACOB), Direction de Recherche Fondamentale (CEA) (DRF (CEA)), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Direction de Recherche Fondamentale (CEA) (DRF (CEA)), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-Institut de Biologie François JACOB (JACOB), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Centre National de la Recherche Scientifique (CNRS), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA), Laboratoire d'Imagerie Paramétrique (LIP), Université Pierre et Marie Curie - Paris 6 (UPMC)-IFR58-Centre National de la Recherche Scientifique (CNRS), Institut d'Imagerie BioMédicale (I2BM), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Université Paris-Saclay, IPSEN Innovation, IPSEN, Sanofi-Aventis R&D, SANOFI Recherche, Neurologie et thérapeutique expérimentale, Université Pierre et Marie Curie - Paris 6 (UPMC)-IFR70-Institut National de la Santé et de la Recherche Médicale (INSERM), CEA [Fontenay-aux-Roses] (UGRA / SETA), Commissariat à l'énergie atomique et aux énergies alternatives (CEA), Université Paris-Saclay-Institut de Biologie François JACOB (JACOB), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Université Paris-Saclay-Institut de Biologie François JACOB (JACOB), and Institut National de la Santé et de la Recherche Médicale (INSERM)-IFR70-Université Pierre et Marie Curie - Paris 6 (UPMC)

Subjects
0301 basic medicine, Pathology, medicine.medical_specialty, [SDV.NEU.NB]Life Sciences [q-bio]/Neurons and Cognition [q-bio.NC]/Neurobiology, Quantitative histopathology, Biology, Article, 03 medical and health sciences, Mice, 0302 clinical medicine, Imaging, Three-Dimensional, Alzheimer Disease, medicine, Image Processing, Computer-Assisted, [SDV.MHEP.AHA]Life Sciences [q-bio]/Human health and pathology/Tissues and Organs [q-bio.TO], Animals, Humans, Image analysis, Brain Mapping, Multidisciplinary, Brain, Gold standard (test), 3. Good health, Disease Models, Animal, 030104 developmental biology, Tissue sections, medicine.anatomical_structure, Open source, Microscopic imaging, 030217 neurology & neurosurgery, Preclinical imaging, Software, Neuroanatomy
Abstract

Histology is the gold standard to unveil microscopic brain structures and pathological alterations in humans and animal models of disease. However, due to tedious manual interventions, quantification of histopathological markers is classically performed on a few tissue sections, thus restricting measurements to limited portions of the brain. Recently developed 3D microscopic imaging techniques have allowed in-depth study of neuroanatomy. However, quantitative methods are still lacking for whole-brain analysis of cellular and pathological markers. Here, we propose a ready-to-use, automated and scalable method to thoroughly quantify histopathological markers in 3D in rodent whole brains. It relies on block-face photography, serial histology and 3D-HAPi (Three Dimensional Histology Analysis Pipeline), an open source image analysis software. We illustrate our method in studies involving mouse models of Alzheimer’s disease and show that it can be broadly applied to characterize animal models of brain diseases, to evaluate therapeutic interventions, to anatomically correlate cellular and pathological markers throughout the entire brain and to validate in vivo imaging techniques.

Published
2016
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26. Robust supervised segmentation of neuropathology whole-slide microscopy images

Author

Frédérique Frouin, Thierry Delzescaux, Yaël Balbastre, Marc Dhenain, Nicolas Souedet, Anne-Sophie Hérard, Michel E. Vandenberghe, Laboratoire des Maladies Neurodégénératives - UMR 9199 (LMN), Service MIRCEN (MIRCEN), Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-Institut de Biologie François JACOB (JACOB), Direction de Recherche Fondamentale (CEA) (DRF (CEA)), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Direction de Recherche Fondamentale (CEA) (DRF (CEA)), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-Institut de Biologie François JACOB (JACOB), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Centre National de la Recherche Scientifique (CNRS), CEA [Fontenay-aux-Roses] (UGRA / SETA), Commissariat à l'énergie atomique et aux énergies alternatives (CEA), Laboratoire d'imagerie fonctionnelle [CHU Pitié-Salpétriêre] (LIF), Université Pierre et Marie Curie - Paris 6 (UPMC)-CHU Pitié-Salpêtrière [AP-HP], Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA), Université Paris-Saclay-Institut de Biologie François JACOB (JACOB), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Université Paris-Saclay-Institut de Biologie François JACOB (JACOB), Laboratoire d'imagerie fonctionnelle [Paris] (LIF), and Université Pierre et Marie Curie - Paris 6 (UPMC)

Subjects
Computer science, Color, Plaque, Amyloid, Neuropathology, Signal-To-Noise Ratio, Preclinical research, Mice, Alzheimer Disease, Microscopy, medicine, Image Processing, Computer-Assisted, Animals, Humans, Computer vision, Segmentation, Pathological, Neuroinflammation, business.industry, Brain, Histology, Neurofibrillary Tangles, Human brain, medicine.disease, 3. Good health, Staining, medicine.anatomical_structure, [SDV.NEU]Life Sciences [q-bio]/Neurons and Cognition [q-bio.NC], Artificial intelligence, Supervised Machine Learning, Alzheimer's disease, business
Abstract

International audience; Alzheimer's disease is characterized by brain pathological aggregates such as Aβ plaques and neurofibrillary tangles which trigger neuroinflammation and participate to neuronal loss. Quantification of these pathological markers on histological sections is widely performed to study the disease and to evaluate new therapies. However, segmentation of neuropathology images presents difficulties inherent to histology (presence of debris, tissue folding, non-specific staining) as well as specific challenges (sparse staining, irregular shape of the lesions). Here, we present a supervised classification approach for the robust pixel-level classification of large neuropathology whole slide images. We propose a weighted form of Random Forest in order to fit nonlinear decision boundaries that take into account class imbalance. Both color and texture descriptors were used as predictors and model selection was performed via a leave-one-image-out cross-validation scheme. Our method showed superior results compared to the current state of the art method when applied to the segmentation of Aβ plaques and neurofibrillary tangles in a human brain sample. Furthermore, using parallel computing, our approach easily scales-up to large gigabyte-sized images. To show this, we segmented a whole brain histology dataset of a mouse model of Alzheimer's disease. This demonstrates our method relevance as a routine tool for whole slide microscopy images analysis in clinical and preclinical research settings.

Published
2015

27. IC‐P‐042: Automated quantification of amyloid load using an atlas‐based analysis in a mouse model of Alzheimer's disease

Author

Hantraye Philippe, Nicolas Souedet, Thierry Delzescaux, Marc Dhenain, Anne-Sophie Hérard, and Michel E. Vandenberghe

Subjects
Internal capsule, Amyloid, Epidemiology, business.industry, Health Policy, Thalamus, Striatum, Ventricular system, Corpus callosum, Psychiatry and Mental health, Cellular and Molecular Neuroscience, medicine.anatomical_structure, Developmental Neuroscience, Cerebral cortex, Medicine, Neurology (clinical), Brainstem, Geriatrics and Gerontology, business, Neuroscience
Abstract

Brainstem 48.39 2.9 0.76 0.1 Cerebral cortex 166.24 1.38 9.78 0.64 Corpus callosum 12.37 0.1 3.52 0.25 Fimbria 3.92 0.06 1.47 0.26 Hippocampal region 26.97 0.46 5.6 0.39 Hypothalamus 7.66 0.4 0.33 0.18 Internal capsule 3.04 0.05 0.12 0.02 Olfactory bulbs NA NA 9.03 0.71 Striatum 32.83 0.46 0.93 0.05 Thalamus 36.72 1.02 0.86 0.14 Ventricular system 4.88 0.07 1.35 0.24 Brain 346.73 4.13 5.58 0.91 Michel Vandenberghe, Anne-Sophie H erard, Nicolas Souedet, Hantraye Philippe, Marc Dhenain, Thierry Delzescaux, Commissariat a l’Energie Atomique et aux Energies Alternatives (CEA), Fontenay-aux-Roses, France; CNRS, Fontenay-aux-Roses, France. Contact e-mail: michel.vandenberghe@cea.fr

Published
2013
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29. Segmentation of small animal PET/CT mouse brain scans using an MRI-based 3D digital atlas

Author

Nicolas Souedet, Thierry Delzescaux, Jessica Lebenberg, Philippe Hantraye, Marie-Claude Gregoire, and Hugo Raguet

Subjects
Biodistribution, Computer science, Image registration, Computed tomography, Automation, Mice, Imaging, Three-Dimensional, Neuroimaging, Atlas (anatomy), medicine, Image Processing, Computer-Assisted, Brain segmentation, Animals, Segmentation, Whole Body Imaging, PET-CT, Brain Mapping, medicine.diagnostic_test, business.industry, Brain, Magnetic resonance imaging, Image segmentation, Magnetic Resonance Imaging, medicine.anatomical_structure, Positron emission tomography, Positron-Emission Tomography, Nuclear medicine, business, Tomography, X-Ray Computed, Algorithms
Abstract

The work reported in this paper aimed at developing and testing an automated method to calculate the biodistribution of a specific PET tracer in mouse brain PET/CT images using an MRI-based 3D digital atlas. Surface-based registration strategy and affine transformation estimation were considered. Such an approach allowed overcoming the lack of anatomical information in the inner regions of PET/CT brain scans. Promising results were obtained in one mouse (on two scans) and will be extended to a neuroinflammation mouse model to characterize the pathology and its evolution. Major improvements are expected regarding automation, time computation, robustness and reproducibility of mouse brain segmentation. Due to its generic implementation, this method could be successfully applied to PET/CT brain scans of other species (rat, primate) for which 3D digital atlases are available.

Published
2010

30. P1‐116: Alterations of brain glucose metabolism in aged APP/PS1 mice: An original voxel‐based statistical analysis using Brain RAT and SPM

Author

Anne‐Sophie Hérard, Albertine Dubois, Nicolas Souedet, Benoît Delatour, Philippe Hantraye, Marc Dhenain, and Thierry Delzescaux

Subjects
Psychiatry and Mental health, Cellular and Molecular Neuroscience, Developmental Neuroscience, Epidemiology, Health Policy, Neurology (clinical), Geriatrics and Gerontology
Published
2009
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31. IC‐P‐008: Alterations of brain glucose metabolism in aged APP/PS1 mice: An original voxel‐based statistical analysis using BrainRAT and SPM

Author

Albertine Dubois, Anne-Sophie Hérard, Philippe Hantraye, Benoît Delatour, Nicolas Souedet, Marc Dhenain, and Thierry Delzescaux

Subjects
medicine.medical_specialty, Epidemiology, Health Policy, Carbohydrate metabolism, Biology, computer.software_genre, Psychiatry and Mental health, Cellular and Molecular Neuroscience, Endocrinology, Developmental Neuroscience, Voxel, Internal medicine, medicine, Statistical analysis, Neurology (clinical), Geriatrics and Gerontology, computer, Neuroscience
Published
2009
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32. Automated Detection Of Highly Aggregated Neurons In Microscopic Images Of Macaque Brain

Author

Anne-Sophie Hérard, Zhenghao Shi, Jimin Liang, Nicolas Souedet, Ming Jiang, Shuangli Du, Thierry Delzescaux, Cheng Shi, Caroline Jan, and Zhenzhen You

Subjects
total number, biology, Computer science, business.industry, microscopic image, Deep learning, Hippocampus, Pattern recognition, neuron detection, Convolutional neural network, Macaque, glial-cells, hippocampus region, medicine.anatomical_structure, cortex, nervous system, multiscale cnn, biology.animal, medicine, Neuron, Artificial intelligence, point annotation, business
Abstract

Neuron detection is a key step in individualizing and counting neurons which are important for assessing physiological and pathophysiological information. A large number of methods including deep learning networks have been proposed but mainly targeting regions with few aggregated neurons. The objective of this paper is to address an automated neuron detection problem in heterogeneous hippocampus region with different degrees of neuron aggregation. Since deep learning networks require a lot of ground truths hut neuron instance annotation is impossible in regions where numerous neurons are clustered, ground truth of centroids marked at the center of neurons is created for training. We propose a multiscale convolutional neural network (CNN) to regress neuron centroid mapping across image. Using multiscale information makes the proposed network applicable not only for single individual neurons, but also for a large number of aggregated neurons. Experimental results show that our method is superior to state-of-the-art deep learning -based algorithms. To our knowledge, this is the first deep learning study to detect neurons in regions of highly clustered neurons.

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