LiTS17 (Liver Tumor Segmentation Challenge 2017)
LiTS17 is a liver tumor segmentation benchmark. The data and segmentations are provided by various clinical sites around the world. The training data set contains 130 CT scans and the test data set 70 CT scans.
The segmentation classes are: Background, Liver and Tumor.
Raw data
Key stats
| Modality | Vision (radiology, CT scans) |
| Task | Segmentation (3 classes) |
| Data size | train: 15GB (53.66 GB uncompressed) |
| Image dimension | ~300 x ~300 x ~350 (number of slices) x 1 (grey scale) * |
| Files format | .nii ("NIFTI") images |
| Number of scans | 131 (58638 slices) |
| Splits in use | train (70%) / val (15%) / test (15%) |
Splits
We use the following random split:
| Splits | Train | Validation | Test |
|---|---|---|---|
| #Scans; Slices | 91; 38686 (77%) | 19; 11192 (11.5%) | 21; 8760 (11.5%) |
Organization
The training data are organized as follows:
Training Batch 1 # Train images part 1
├── segmentation-0.nii # Semantic labels for volume 0
├── segmentation-1.nii # Semantic labels for volume 1
├── ...
├── volume-0.nii # CT-Scan 0
├── volume-1.nii # CT-Scan 1
└── ...
Training Batch 2 # Train images part 2
├── segmentation-28.nii # Semantic labels for volume 28
├── segmentation-29.nii # Semantic labels for volume 29
├── ...
├── volume-28.nii # CT-Scan 28
├── volume-29.nii # CT-Scan 29
└── ...
Download and preprocessing
The LiTS dataset can be downloaded from the official
LiTS competition page.
The training split comes into two .zip files, namely Training_Batch1.zip
and Training_Batch2.zip, which should be extracted and merged.