Leslie Tricoche, Denis Pélisson, Léa Longo, Eric Koun, Alice Poisson, Jérôme Prado, Martine Meunier
Task-independent neural bases of peer presence effect on cognition in children and adults
NeuroImage
accurate segmentation individual muscles essential quantitative mri analysis thigh images deep learning methods achieved stateoftheart results segmentation require large numbers labeled data perform well however labeling individual thigh muscles slice slice numerous volumes laborious timeconsuming task limits availability annotated datasets address challenge selfsupervised learning ssl emerges promising technique enhance model performance pretraining model unlabeled data recent approach called positional contrastive learning exploits information given axial position slices learn features transferable segmentation task aim work propose positional contrastive ssl segmentation individual thigh muscles mri acquisitions population elderly healthy subjects evaluate different levels limited annotated data unlabeled dataset 72 t1w mri thigh acquisitions available ssl pretraining labeled dataset 52 volumes employed final segmentation task split training test sets effectiveness ssl pretraining finetune unet architecture thigh muscle segmentation compared randomly initialized model rnd considering increasing number annotated volumes 1 2 5 10 20 30 40 results demonstrated ssl yields substantial improvements dice similarity coefficient dsc using limited number labeled volumes eg 1 dsc 0631 versus 0530 ssl rnd respectively moreover enhancements achievable even utilizing full number labeled subjects dsc 0927 ssl 0924 rnd conclusion positional contrastive ssl effective obtaining accurate thigh muscle segmentation even low number labeled data potential impact speeding annotation process clinics