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Sélection de caractéristiques stables pour la segmentation d'images histologiques par calcul haute performance

Clément Bouvier 1
1 LMN - Laboratoire des Maladies Neurodégénératives - UMR 9199
MIRCEN - Service MIRCEN : DRF/JACOB, CNRS - Centre National de la Recherche Scientifique : UMR 9199
Abstract : In preclinical research and more specifically in neurobiology, histology uses images produced by increasingly powerful optical microscopes digitizing entire sections at cell scale. Quantification of stained tissue such as neurons relies on machine learning driven segmentation. However such methods need a lot of additional information, or features, which are extracted from raw data multiplying the quantity of data to process. As a result, the quantity of features is becoming a drawback to process large series of histological images in a fast and robust manner. Feature selection methods could reduce the amount of required information but selected subsets lack of stability. We propose a novel methodology operating on high performance computing (HPC) infrastructures and aiming at finding small and stable sets of features for fast and robust segmentation on high-resolution histological whole sections. This selection has two selection steps: first at feature families scale (an intermediate pool of features, between space and individual feature). Second, feature selection is performed on pre-selected feature families. In this work, the selected sets of features are stables for two different neurons staining. Furthermore the feature selection results in a significant reduction of computation time and memory cost. This methodology can potentially enable exhaustive histological studies at a high-resolution scale on HPC infrastructures for both preclinical and clinical research settings.
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Submitted on : Wednesday, January 20, 2021 - 1:01:17 AM
Last modification on : Thursday, January 21, 2021 - 3:29:32 AM


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  • HAL Id : tel-03115972, version 1


Clément Bouvier. Sélection de caractéristiques stables pour la segmentation d'images histologiques par calcul haute performance. Bio-informatique [q-bio.QM]. Sorbonne Université, 2019. Français. ⟨NNT : 2019SORUS004⟩. ⟨tel-03115972⟩



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