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Détection automatique du nerf dans les images échographiques

Abstract : Regional anesthesia presents an interesting alternative or complementary act to general anesthesia in many surgical procedures. It reduces pain scores, improves postoperative mobility and facilitates earlier hospital discharge. Ultrasound-Guided Regional Anesthesia (UGRA) has been gaining importance in the last few years, offering numerous advantages over alternative methods of nerve localization (neurostimulation or paraesthesia). However, nerve detection is one of the most difficult tasks that anesthetists can encounter in the UGRA procedure. The context of the present work is to provide practitioners with a method to facilitate and secure the practice of UGRA. However, automatic detection and segmentation in ultrasound images is still a challenging problem in many medical applications. This work addresses two main issues. The first one, we propose an algorithm for nerve detection and segmentation in ultrasound images, this method is composed of a pre-processing, texture analysis and machine learning steps. In this part of work, we explore two new approaches ; one to characterize the nerve and the second for selecting the minimum redundant and maximum relevant features. The second one, we studied the nerve detection in consecutive ultrasound frames. We have demonstrated that the development of an algorithm based on the temporal coherence of the position, the shape and the confidence measure of the classification, allows to generate a robust segmentation. In this work, we also propose a new model of shape based on a set of intervals landmarks able to adapt to the nerve shape under a morphological variations.
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https://tel.archives-ouvertes.fr/tel-01713114
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Submitted on : Tuesday, February 20, 2018 - 11:05:05 AM
Last modification on : Wednesday, November 20, 2019 - 1:42:50 AM
Long-term archiving on: : Monday, May 21, 2018 - 12:29:02 PM

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  • HAL Id : tel-01713114, version 1

Citation

Oussama Hadjerci. Détection automatique du nerf dans les images échographiques. Vision par ordinateur et reconnaissance de formes [cs.CV]. Université d'Orléans, 2017. Français. ⟨NNT : 2017ORLE2006⟩. ⟨tel-01713114⟩

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