From: 3D AGSE-VNet: an automatic brain tumor MRI data segmentation framework
Team | Dice | Sensitivity | Specificity | Hausdorff95 | ||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|
ET | WT | TC | ET | WT | TC | ET | WT | TC | ET | WT | TC | |
Proposed | 0.68 | 0.85 | 0.69 | 0.68 | 0.83 | 0.65 | 0.99 | 0.99 | 0.99 | 47.40 | 8.44 | 31.60 |
mpstanford | 0.49 | 0.72 | 0.62 | 0.49 | 0.81 | 0.69 | 0.99 | 0.99 | 0.99 | 61.89 | 26.00 | 28.02 |
agussa | 0.59 | 0.83 | 0.69 | 0.60 | 0.87 | 0.71 | 0.99 | 0.99 | .0.99 | 56.58 | 23.23 | 29.59 |
ovgu_seg | 0.60 | 0.79 | 0.68 | 0.66 | 0.79 | 0.67 | 0.99 | 0.99 | 0.99 | 54.07 | 12.05 | 19.10 |
AI-Strollers | 0.58 | 0.74 | 0.61 | 0.52 | 0.77 | 0.62 | 0.99 | 0.99 | 0.99 | 47.23 | 24.03 | 31.54 |
uran | 0.75 | 0.88 | 0.76 | 0.77 | 0.85 | 0.71 | 0.99 | 0.99 | 0.99 | 36.42 | 6.62 | 19.30 |
CBICA | 0.63 | 0.82 | 0.67 | 0.76 | 0.78 | 0.75 | 0.99 | 0.99 | 0.99 | 9.60 | 10.70 | 28.20 |
unet3d-sz | 0.70 | 0.84 | 0.72 | 0.71 | 0.87 | 0.79 | 0.99 | 0.99 | 0.99 | 42.09 | 10.48 | 12.32 |
iris | 0.68 | 0.86 | 0.73 | 0.67 | 0.90 | 0.70 | 0.99 | 0.99 | 0.99 | 44.13 | 23.87 | 20.02 |
VuongHN | 0.79 | 0.90 | 0.83 | 0.80 | 0.89 | 0.80 | 0.99 | 0.99 | 0.99 | 21.43 | 6.74 | 7.05 |