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17 décembre 2020

Deep Learning-based Low-limb Muscle Segmentation for Reproducible Volume Quantification

Catégorie : Stagiaire

Abstract : The role of the recruited trainee is to design an approach to provide a fast and automatic

estimation of the muscle volume of elite athletes from MRI images. The internship will be funded by the FULGUR project.

The goal of the internship is to estimate the volume of the muscles involved in sprinting performance (hamstring, gluteus, adductor magnus, and quadriceps). To this we will record MRI images of a group of elite athletes, and segment the muscles in the acquired images. Although MRI provides accurate volumetric measurements, the segmentation requires the time-consuming delineation of muscle on each MR slice.

This projet aims at accelerating such segmentation, through interactive learning methods, relying on recent advances in the field by the LS2N laboratory and the Ecole Centrale de Nantes. The existing approach was developed for the accurate automatic delimitation of each muscle on ultrasound images. The role of the recruited trainee will be to transfer this approach to MRI images

Position start January/Febrary 2021

Duration : 6 months

Net salary ~550€ / month

Contact: dawood.alchanti@ls2n.fr , diana.mateus@ls2n.fr, lilian.lacourpaille@univ-nantes.fr

See a full description of the topic in this link: https://box.ec-nantes.fr/index.php/s/7LZFwQTKskkXE6b


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