In this talk, we present Skeleton Recall Loss, a novel loss function for topologically accurate and efficient segmentation of thin, tubular structures, such as roads, nerves, or vessels. By circumventing expensive GPU-based operations, we reduce computational overheads by up to 90% compared to the current state-of-the-art, while achieving overall superior performance in segmentation accuracy and connectivity preservation. Additionally, it is the first multi-class capable loss function for thin structure segmentation.
ECCV 2024 Paper
About the Speakers
Maximilian Rokuss holds a M.Sc. in Physics from Heidelberg University, now PhD Student in Medical Image Computing at German Cancer Research Center (DKFZ) and Heidelberg University
Yannick Kirchoff holds a M.Sc. in Physics from Heidelberg University, now PhD Student in Medical Image Computing at German Cancer Research Center (DKFZ) and Helmholtz Information and Data Science School for Health
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