This study propose an improved automatic segmentation of longitudinal MRI dataset of mountain ultra-marathon runners’ upper thighs acquired during the Tor des Géants 2014 by using a multi-atlas segmentation strategy with corrective learning with a small number of training set. Our highly accurate and robust segmentations allow us to locally study the inflammation of each quadriceps head induced by the extreme conditions of the race, a method that is of high interest to monitor the impact of eccentric efforts during the race, identify local physiopathology changes in patients, and benefits of eventual therapy or intervention.
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