Table 4.
Details of the participating teams’ methods in LiTS-ISBI-2017.
| Lead author & team members |
Method, Architecture & Modifications |
Data augmentation | Loss function | Optimizer training | Pre-processing | Post-processing | Ensemble strategy |
|---|---|---|---|---|---|---|---|
| ■ X. Han; | Residual U-Net with 2.5D input (a stack of 5 slices) | cropping, flipping | weighted cross-entropy | SGD with momentum | Value-clipping to [−200, 200] | Connected components with maximum probability below 0.8 were removed | None |
| ■ E. Vorontsov; A. Tang, C. Pal, S. Kadoury | Liver FCN provides pre-trained weights for tumor FCN. Trained on 256 × 256, finetuned on 512 × 512. | Random flips, rotations, zooming, elastic deformations. | Dice loss | RMSprop | None | largest connected component for liver | Ensemble of three models |
| ■ G. Chlebus; H. Meine, J. H. Moltz, A. Schenk | Liver: 3 orthogonal 2D U-nets working on four resolution levels. Tumor: 2D U-net working on four resolution levels |
Manual removal of cases with flawed reference segmentation | soft Dice | Adam | Liver: resampling to isotropic 2 mm voxels. | Tumor candidate filtering based on RF-classifier to remove false positives | For liver: majority vote |
| ■L. Bi; Jinman Kim | Cascaded ResNet based on a pre-trained FCN on 2D axial slices. | Random scaling, crops and flips | cross-entropy | SGD | Value-clipping to [−160, 240] | Morphological filter to fill the holes | Multi-scale ensembling by averaging the outputs from. different inputs sizes. |
| ■ C. Wang | Cascaded 2D U-Net in three orthogonal | Random rotation, random translation, and scaling | Soft Dice | SGD | None | None | None |
| ■ P. Christ | views Cascaded U-Net | mirror, cropping, additional noise | weighted cross-entropy | SGD with momentum | 3D Conditional Random Field | ||
| ■ J. Lipkova; M. Rempfler, J. Lowengrub, B. Menze | U-Net for liver segmentation and Cahn-Hilliard Phase field separation for lesions | None | Liver:cross-entropy; Tumor: Energy function | SGD | None | None | None |
| ■J. Ma; Y. Li, Y. Wu, M. Zhang, X. Yang | Random Forest and Fuzzy Clustering | None | None | None | Value-clipping to [−160, 240] and intensity normalization to [0,255] | ||
| ■ T. Konopczynski; K. Roth, J. Hesser | Dense 2D U-Nets (Tiramisu) | None | Soft Tversky-Coefficient based Loss function | Adam | Value-clipping to [−100, 400] | None | None |
| ■M. Bellver; K. Maninis, J. Tuset, X. Giro-i-Nieto, J. Torres | Cascaded FCN with side outputs at different resolutions. Three-channel 2D input. | None | Weighted binary cross entropy | SGD with Momentum | Value-clipping to [−150, 250] and intensity normalization to [0,255] | Component analysis to remove false positives. 3D CRF. | None |
| ■ J. Qi; M. Yue | A pretrained VGG with concatenated multi-scale feature maps | None | binary cross entropy | SGD | None | None | None |