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Brain Tissue Segmentation on iSEG challenge 2017 (test)

96CSF DSCEarlyFusion_Ensemble
89.957691.526393.09594.6637Dec 14, 2017Aug 29, 2018May 15, 2019Jan 29, 2020Oct 14, 2020Jun 30, 2021Mar 16, 2022

Evaluation results

MethodLinks
EarlyFusion_EnsembleArchitecture=Ensemble of CNNs, Fusion type=Early2017.12969.030.14926.420.34906.980.38—
LateFusion_EnsembleArchitecture=Ensemble of CNNs, Fusion type=Late2017.12969.130.12926.060.34907.450.41—
Bern_IPMI2017.12969.620.13926.460.34906.780.4—
MSL-SKKU2017.12969.070.12925.980.33906.440.39—
BCH_CRL_IMAGINE2018.04968.850.1192.69.5570.31190.77.1040.36—
MSL_SKKU2018.0495.89.1120.11692.35.9990.32190.46.6180.375—
Qamar et al.Depth=822022.0395.8——92.05——90.5——92.77
HyperDenseNet2018.0495.69.4210.12925.7520.32990.16.660.382—
EarlyFusion_SingleArchitecture=Single CNN, Fusion type=Early2017.12959.30.13927.130.35906.90.41—
nic_vicorob2017.12959.180.14917.650.37897.150.43—
TU/e IMAG/e2017.12959.430.15906.860.38896.910.43—
3D-SkipDenseSegDepth=472022.0394.88——91.64——91.02——92.51
3D-UnetDepth=182022.0394.39——90.55——89.83——91.59
VoxResNetDepth=252022.0394.28——90.64——89.87——91.6
3D-UCapsDepth=172022.0394.21——91.34——90.95——92.17
3D-SegCapsDepth=162022.0393.62——88.53——86.49——89.55
CC-3D-FCNDepth=342022.0392.4——90.74——89.19——90.79
CatholicU2018.0491.610.970.24184.27.2830.54681.98.2390.675—
DenseVoxNetDepth=322022.0391.26——88.51——85.46——89.24
SegCaps (2D)Depth=162022.0390.19——84.19——82.8——85.73