Table 5.
Details of the participating teams’ methods in LiTS-MICCAI-2017.
| Lead author & team members |
Method, Architecture & Modifications |
Data augmentation | Loss function | Optimizer training | Pre-processing | Post-processing | Ensemble strategy |
|---|---|---|---|---|---|---|---|
| ■Y. Yuan; | Hierarchical 2.5D FCN network | flipping, shifting, rotating, scaling and random contrast normalization | Jaccard distance | Adam | Clipping HU values to [−100, 400] | None | ensembling 5 models from 5-fold cross validation |
| ■ A. Ben-Cohen; | VGG-16 as a backbone and 3-channel input | scaling | softmax log loss | SGD | Clipping HU values to [−160, 240] | None | None |
| ■ J. Zou; | Cascaded U-Nets | weighted cross-entropy | Adam | Clipping HU values to [−75, 175] | hole filling and noise removal | ensemble of two model with different inputs | |
| ■ X. Li, H. Chen, X. Qi, Q. Dou, C. Fu, P. Heng | H-DenseUNet (Li et al., 2018a) | rotation, flipping, scaling | Cross-entropy | SGD | Clipping HU values to [−200, 250] | Largest connected component; hole filling | None |
| ■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 (Chlebus et al., 2018) | 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 |
| ■ J. Wu | Cascade 2D FCN | scaling | Dice Loss | Adam | None | None | None |
| ■C. Wang | Cascade 2D U-Net in three orthogonal views | Random rotation, random translation, and scaling | Soft Dice | SGD | None | None | 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 |
| ■ K. Roth; T. Konopczynski, J. Hesser | 2D and 3D U-Net, however with an iterative Mask Mining process similar to model boosting. | flipping, rotation, and zooming | Mixture of smooth Dice loss and weighted cross-entropy | Adam | Clipping HU values to [−100, 600] | None | None |
| ■ 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 |
| ■ J. Lipkova M. Rempfler, J. Lowengrub | U-Net for liver segmentation and Cahn-Hilliard Phase field separation for lesions | None | Liver:cross-entropy; Tumor: Energy function | SGD | None | None | None |
| ■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. |
| ■M. Piraud A. Sekuboyina, B. Menze | U-Net with a double sigmoid activation | None | weighted cross-entropy | Adam | Value-clipping to [−100, 400]; intensity normalization | None | None |
| ■J. Ma Y. Li, Y. Wu M. Zhang, X. Yang | Label propagation (Interactive method) | None | None | None | Value-clipping to [−100, 400]; intensity normalization | None | None |
| ■L. Zhang; S. C. YU | Context-aware PolyUNet with zooming out/in and two-stage strategy (Zhang and Yu, 2021) | Weighted cross-entropy | SGD | Value-clipping to [−200, 300] | largest connected component | None |