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Automatisation de la segmentation sémantique de structures cardiaques en imagerie ultrasonore par apprentissage supervisé

Sarah Marie-Solveig Leclerc 1, 2
2 Images et Modèles
CREATIS - Centre de Recherche en Acquisition et Traitement de l'Image pour la Santé
Abstract : The analysis of medical images plays a critical role in cardiology. Ultrasound imaging, as a real-time, low cost and bed side applicable modality, is nowadays the most commonly used image modality to monitor patient status and perform clinical cardiac diagnosis. However, the semantic segmentation (i.e the accurate delineation and identification) of heart structures is a difficult task due to the low quality of ultrasound images, characterized in particular by the lack of clear boundaries. To compensate for missing information, the best performing methods before this thesis relied on the integration of prior information on cardiac shape or motion, which in turns reduced the adaptability of the corresponding methods. Furthermore, such approaches require man- ual identifications of key points to be adapted to a given image, which makes the full process difficult to reproduce. In this thesis, we propose several original fully-automatic algorithms for the semantic segmentation of echocardiographic images based on supervised learning ap- proaches, where the resolution of the problem is automatically set up using data previously analyzed by trained cardiologists. From the design of a dedicated dataset and evaluation platform, we prove in this project the clinical applicability of fully-automatic supervised learning methods, in particular deep learning methods, as well as the possibility to improve the robustness by incorporating in the full process the prior automatic detection of regions of interest.
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Submitted on : Thursday, July 16, 2020 - 11:11:16 AM
Last modification on : Wednesday, October 14, 2020 - 4:10:00 AM


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  • HAL Id : tel-02900524, version 1


Sarah Marie-Solveig Leclerc. Automatisation de la segmentation sémantique de structures cardiaques en imagerie ultrasonore par apprentissage supervisé. Traitement du signal et de l'image [eess.SP]. Université de Lyon, 2019. Français. ⟨NNT : 2019LYSEI121⟩. ⟨tel-02900524⟩



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