Public articles linked to the same research event.
medRxiv This preprint benchmarks eight open-source thigh muscle MRI segmentation tools on the MyoSegmenTUM, AIPS and Sheffield base datasets plus derived pathological and augmented out-of-distribution sets, combining Dice, Jaccard, Hausdorff, boundary IoU and inter-slice Dice ratio with qualitative usability review, and finds that domain-specific U-Net models, especially MuscleMap, generally outperformed foundation-model and newer general-purpose approaches, that adding SAM variants usually degraded rather than improved segmentation quality, and that most tools showed reduced accuracy on pathological cases.
This preprint benchmarks eight open-source thigh muscle MRI segmentation tools on the MyoSegmenTUM, AIPS and Sheffield base datasets plus derived pathological and augmented out-of-distribution sets, combining Dice, Jaccard, Hausdorff, boundary IoU and inter-slice Dice ratio with qualitative usability review, and finds that domain-specific U-Net models, especially MuscleMap, generally outperformed foundation-model and newer general-purpose approaches, that adding SAM variants usually degraded rather than improved segmentation quality, and that most tools showed reduced accuracy on pathological cases.
This preprint benchmarks eight open-source thigh muscle MRI segmentation tools on the MyoSegmenTUM, AIPS and Sheffield base datasets plus derived pathological and augmented out-of-distribution sets, combining Dice, Jaccard, Hausdorff, boundary IoU and inter-slice Dice ratio with qualitative usability review, and finds that domain-specific U-Net models, especially MuscleMap, generally outperformed foundation-model and newer general-purpose approaches, that adding SAM variants usually degraded rather than improved segmentation quality, and that most tools showed reduced accuracy on pathological cases.
This preprint benchmarks eight open-source thigh muscle MRI segmentation tools on the MyoSegmenTUM, AIPS and Sheffield base datasets plus derived pathological and augmented out-of-distribution sets, combining Dice, Jaccard, Hausdorff, boundary IoU and inter-slice Dice ratio with qualitative usability review, and finds that domain-specific U-Net models, especially MuscleMap, generally outperformed foundation-model and newer general-purpose approaches, that adding SAM variants usually degraded rather than improved segmentation quality, and that most tools showed reduced accuracy on pathological cases.