Table 5. Comparison with the state-of-the-art methods in dataset B.
| Network | DSC | AER | HE | MAE |
|---|---|---|---|---|
| FCN-AlexNet (34) | 0.84 | 0.39 | 25.1 | 7.1 |
| SegNet (35) | 0.89 | 0.22 | 21.7 | 4.5 |
| CE-Net (36) | 0.90 | 0.22 | 21.6 | 4.5 |
| SCAN (37) | 0.90 | 0.20 | 26.9 | 4.9 |
| DenseU-net (38) | 0.88 | 0.25 | 25.3 | 5.5 |
| MultiResUNet (39) | 0.91 | 0.19 | 18.8 | 4.1 |
| STAN (40) | 0.91 | 0.18 | 18.9 | 3.9* |
| Fuzzy FCN (41) | 0.92 | 0.14* | 19.8 | 4.2 |
| Huang et al. (42) | 0.93* | 0.15 | 26.0 | 4.9 |
| GDUNet | 0.93* | 0.14* | 17.6* | 4.7 |
*, best result in the table. DSC, Dice similarity coefficient; AER, area error ratio; HE, Hausdorff error; MAE, mean absolute error; FCN, fully convolutional network; SegNet, deep fully convolutional neural network architecture for semantic pixel-wise segmentation; CE-Net, context encoder network; SCAN, semantic context-aware network; MultiResUNet, the U-Net Architecture for Multimodal Biomedical Image Segmentation; STAN, small tumor-aware network; GDUNet, attention gate and dilation U-shaped network.