Impact of Different Techniques for Slice Annotation Reduction on U-Net-Based Thigh Muscle MR Images Segmentation

Motivation: Deep Learning (DL) for thigh muscle segmentation in MR images holds promise for musculoskeletal architectural assessment, however the process of generating annotated data in supervised approaches is time-consuming. Goal(s): This study evaluates the impact of scarce annotated data on DL segmentation performance, investigating optimal annotation strategies of thigh muscle MR images. Approach: Employing thigh MRIs from healthy subjects, the research compares the segmentation performance using various selection strategies and annotated data amount for training a U-Net. Results: Results reveal high segmentation accuracy (Dice > 0.81) even with minimal annotations (3% of total labels), when selecting the most informative slices for annotation. Impact: This research highlights the potential of significantly reducing the laborious task of annotating MR images for thigh muscle segmentation, while maintaining robust performance using DL. This efficiency enhancement could expedite the application of DL in muscle health assessment.