Brain Tissue Segmentation on iSEG challenge 2017 (test) benchmark leaderboard · SOTA2 Research

Brain Tissue Segmentation on iSEG challenge 2017 (test)

96CSF DSCEarlyFusion_Ensemble

Evaluation Results

Method	Links
EarlyFusion_Ensemble2017.12		96	9.03	0.14	92	6.42	0.34	90	6.98	0.38	—
LateFusion_Ensemble2017.12		96	9.13	0.12	92	6.06	0.34	90	7.45	0.41	—
Bern_IPMI2017.12		96	9.62	0.13	92	6.46	0.34	90	6.78	0.4	—
MSL-SKKU2017.12		96	9.07	0.12	92	5.98	0.33	90	6.44	0.39	—
BCH_CRL_IMAGINE2018.04		96	8.85	0.11	92.6	9.557	0.311	90.7	7.104	0.36	—
MSL_SKKU2018.04		95.8	9.112	0.116	92.3	5.999	0.321	90.4	6.618	0.375	—
Qamar et al.2022.03		95.8	—	—	92.05	—	—	90.5	—	—	92.77
HyperDenseNet2018.04		95.6	9.421	0.12	92	5.752	0.329	90.1	6.66	0.382	—
EarlyFusion_Single2017.12		95	9.3	0.13	92	7.13	0.35	90	6.9	0.41	—
nic_vicorob2017.12		95	9.18	0.14	91	7.65	0.37	89	7.15	0.43	—
TU/e IMAG/e2017.12		95	9.43	0.15	90	6.86	0.38	89	6.91	0.43	—
3D-SkipDenseSeg2022.03		94.88	—	—	91.64	—	—	91.02	—	—	92.51
3D-Unet2022.03		94.39	—	—	90.55	—	—	89.83	—	—	91.59
VoxResNet2022.03		94.28	—	—	90.64	—	—	89.87	—	—	91.6
3D-UCaps2022.03		94.21	—	—	91.34	—	—	90.95	—	—	92.17
3D-SegCaps2022.03		93.62	—	—	88.53	—	—	86.49	—	—	89.55
CC-3D-FCN2022.03		92.4	—	—	90.74	—	—	89.19	—	—	90.79
CatholicU2018.04		91.6	10.97	0.241	84.2	7.283	0.546	81.9	8.239	0.675	—
DenseVoxNet2022.03		91.26	—	—	88.51	—	—	85.46	—	—	89.24
SegCaps (2D)2022.03		90.19	—	—	84.19	—	—	82.8	—	—	85.73