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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