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A priori et apprentissage profond pour la segmentation en imagerie cérébrale

Abstract : Medical imaging is a vast field guided by advances in instrumentation, acquisition techniques and image processing. Advances in these major disciplines all contribute to the improvement of the understanding of both physiological and pathological phenomena. In parallel, access to broader imaging databases, combined with the development of computing power, has fostered the development of machine learning methodologies for automatic image processing, including approaches based on deep neural networks. Among the applications where deep neural networks provide solutions, we find image segmentation, which consists in locating and delimiting in an image regions with specific properties that will be associated with the same structure. Despite many recent studies in deep learning based segmentation, learning the parameters of a neural network is still guided by quantitative performance measures that do not include high-level knowledge of anatomy. The objective of this thesis is to develop methods to integrate a priori into deep neural networks, targeting the segmentation of brain structures in MRI imaging. Our first contribution proposes a strategy for integrating the spatial position of the patch to be classified, to improve the discriminating power of the segmentation model. This first work considerably corrects segmentation errors that are far away from the anatomical reality, also improving the overall quality of the results. Our second contribution focuses on a methodology to constrain adjacency relationships between anatomical structures, directly while learning network parameters, in order to reinforce the realism of the produced segmentations. Our experiments conclude that the proposed constraint corrects non-admitted adjacencies, thus improving the anatomical consistency of the segmentations produced by the neural network.
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Submitted on : Thursday, September 10, 2020 - 9:02:49 AM
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Pierre-Antoine Ganaye. A priori et apprentissage profond pour la segmentation en imagerie cérébrale. Traitement du signal et de l'image [eess.SP]. Université de Lyon, 2019. Français. ⟨NNT : 2019LYSEI100⟩. ⟨tel-02935104⟩

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