Table 2 The results of various indicators in the training set
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 | 0.70 | 0.85 | 0.77 | 0.72 | 0.83 | 0.74 | 0.99 | 0.99 | 0.99 | 35.70 | 8.96 | 17.40 |
mpstanford | 0.60 | 0.78 | 0.72 | 0.56 | 0.80 | 0.75 | 0.99 | 0.99 | 0.99 | 35.95 | 17.68 | 17.21 |
agussa | 0.67 | 0.87 | 0.79 | 0.69 | 0.87 | 0.82 | 0.99 | 0.99 | 0.99 | 39.25 | 15.75 | 17.05 |
ovgu_seg | 0.65 | 0.81 | 0.75 | 0.72 | 0.78 | 0.76 | 0.99 | 0.99 | 0.99 | 34.79 | 9.50 | 8.93 |
AI-Strollers | 0.59 | 0.73 | 0.61 | 0.52 | 0.73 | 0.64 | 0.99 | 0.97 | 0.98 | 38.87 | 20.81 | 24.22 |
uran | 0.48 | 0.79 | 0.64 | 0.45 | 0.74 | 0.61 | 0.99 | 0.99 | 0.99 | 37.92 | 7.72 | 14.07 |
CBICA | 0.54 | 0.78 | 0.57 | 0.64 | 0.82 | 0.53 | 0.99 | 0.99 | 0.99 | 20.00 | 46.30 | 39.60 |
unet3d-sz | 0.69 | 0.81 | 0.75 | 0.77 | 0.93 | 0.83 | 0.99 | 0.96 | 0.98 | 37.71 | 19.57 | 18.36 |
iris | 0.76 | 0.88 | 0.81 | 0.78 | 0.90 | 0.83 | 0.99 | 0.99 | 0.99 | 32.30 | 18.07 | 14.70 |
VuongHN | 0.74 | 0.81 | 0.82 | 0.84 | 0.98 | 0.84 | 0.95 | 0.93 | 0.99 | 21.97 | 12.32 | 8.72 |