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Multi-organ Segmentation on BTCV (5-fold cross-validation)

95.92SplnnUNet
90.127291.631193.13594.6389Oct 30, 2024Jan 31, 2025May 5, 2025Aug 7, 2025Nov 8, 2025Feb 10, 2026May 15, 2026

Evaluation results

MethodLinks
nnUNetTraining type=Dataset-specific2026.0595.9290.4566.5875.7196.4986.0588.3382.7278.3179.1767.9982.01
CoTrTraining type=Dataset-specific2026.0595.5188.6168.4975.8395.9381.8489.0182.3273.3975.1265.7880.48
SwinUNetrTraining type=Dataset-specific2026.0595.4493.3977.1274.1496.3980.1290.0282.9375.0881.0264.9882.06
Qwen2.5VL-3BArchitecture=Qwen2.5VL-3B, Training type=Dataset-agnostic2026.0595.3290.7376.9975.9692.3686.2788.881.5476.6879.3869.7283.07
UNETRTraining type=Dataset-specific2026.0594.9192.6176.9874.0196.1779.9889.7481.275.0580.1262.681.43
TransBTSTraining type=Dataset-specific2026.0594.5989.8568.575.5996.1483.7288.8582.2874.2575.1266.7480.94
nnFormerTraining type=Dataset-specific2026.0594.5190.9465.5174.4996.183.8388.9180.5875.9477.7168.1981.22
TransUNetTraining type=Dataset-specific2026.0594.189.5365.4973.1993.2480.8587.4780.4871.4774.2664.7679.16
Janus1BArchitecture=Janus-1B, Training type=Dataset-agnostic2026.0593.9889.7978.0576.0293.4285.3386.5982.9673.7480.4465.7882.31
S3DPre-training Method=S3D, Network=ResEncL (fixed) [21]2024.109290.470.7778.7196.0192.5192.8387.0477.2884.7972.5884.99
No (Fix.)Pre-training Method=No (Fix.), Network=ResEncL (fixed) [21]2024.1091.9789.5868.7679.1895.9691.9792.887.1677.2984.0172.2184.63
HySparKPre-training Method=HySparK [33], Network=MedNeXt+ViT [33]2024.1090.9486.9963.4374.3995.1287.1588.9283.4872.7779.6664.8480.67
No (Dyn.)Pre-training Method=No (Dyn.), Network=nnU-Net [20]2024.1090.4488.5268.8678.1495.5388.0691.5986.4776.2781.7871.0683.34
NaPre-training Method=Na, Network=MedNeXt+ViT [33]2024.1090.3587.4663.1874.4995.098689.2983.2271.8579.4862.5980.27