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Abstract #3174

Automatic muscle segmentation of MRI images of mice with nnU-net

Beatrice Matot1, Louis Rigler1, Rahma Ait-Ouaret1, Caroline Prot-Bertoye2,3, Camille Griveau2,3, Gaelle Brideau2,3, Benjamin Marty1, Harmen Reyngoudt1, Yves Fromes1, and Pierre-Yves Baudin1
1Neuromuscular Investigation Center, NMR Laboratory, Institute of Myology, Paris, France, 2Centre de Recherche des Cordeliers, Institut National de la Santé et de la Recherche Médicale, Sorbonne Université, Université Paris Cité, F-75006 Paris, France, 3Centre National de la Recherche Scientifique, Equipe Mixte de Recherche 8228-Laboratoire de Physiologie Rénale et Tubulopathies, F-75006 Paris, France

Synopsis

Keywords: Small Animals, Muscle, Segmentation, Preclinical

Motivation: Muscle or muscle groups segmentation is a crucial step in quantitative MRI pipeline to provide biomarkers of muscle disorders or aging but it is a time consuming step.

Goal(s): The aim of this project is to leverage automatic muscle segmentation models in clinical research to translate them in preclinical research.

Approach: We hypothesize that nnU-net, a deep-learning automatic segmentation framework, can be effectively used for studying aging and muscle disorders in mouse models.

Results: Our results show that nnU-net model can be translated to mouse hind legs muscle segmentation.

Impact: Our automatic muscle segmentation tool significantly speeds up the processing duration of MRI data and therefore the development of new quantitative MRI outcomes in the field of neuromuscular disorders.

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Keywords