Table 1.
Top-10 finalists after statistical ranking. “Value” represents the average rank the algorithm achieved across all tasks. We also show if methods were automated, used external data for training, the input data dimensions used in the algorithms, and the network architecture.
| Rank | Value | ID # | Fully automated |
Extra data |
Pre-trained | Ensemble | Data dimension |
Network architecture |
Authors | Country |
|---|---|---|---|---|---|---|---|---|---|---|
| 1 | 2.6 | 53 | ✓ | ✓ | ✗ | ✗ | 3D | nnU-Net | S. Hu et al. | China |
| 2 | 6.0 | 38 | ✓ | ✗ | ✗ | ✓ | 3D | nnU-Net | F. Isensee et al. | Germany |
| 3 | 7.7 | 65 | ✓ | ✗ | ✗ | ✓ | 2D/3D | nnU-Net | C. Tang | USA |
| 4 | 8.4 | 58 | ✓ | ✗ | ✗ | ✓ | 3D | nnU-Net | Q. Yu et al. | China |
| 5 | 8.5 | 31 | ✓ | ✗ | ✗ | ✓ | 3D | nnU-Net | J. Sölter et al. | Luxembourg |
| 6 | 9.2 | 50 | ✓ | ✗ | ✗ | ✓ | 2D/3D | nnU-Net | T. Zheng & L. Zhang | Japan |
| 6 | 9.2 | 68 | ✓ | ✗ | ✓ | ✗ | 2D/3D | VGG16 Hybrid, MONAI |
V. Liauchuk et al. | Belarus |
| 8 | 9.4 | 95 | ✓ | ✗ | ✗ | ✓ | 3D | nnU-Net | Z. Zhou et al. | China |
| 9 | 10.6 | 29 | ✓ | ✗ | ✗ | ✗ | 3D | nnU-Net | J. Moltz et al. | Germany |
| 10 | 11.3 | 15 | ✓ | ✗ | ✗ | ✗ | 3D | U-Net | B. Oliveira et al. | Portugal |