Skip to content

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 (77%) / val (11.5%) / test (11.5%)

Splits

We use the splits according to the KiU-Net:

Splits Train Validation Test
#Scans; Slices 101; 39307 (77%) 15; 12045 (11.5%) 15; 7286 (11.5%)

The split is conducted linearly; the first 101 scans are allocated to the train set, the next 15 to validation and the final 15 to test.

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.

License

CC BY-NC-ND 4.0