DATA FORMAT All files are stored in Nifti-1 format with 32-bit floating point data.
Images are stored as 'volume-XX.nii.gz' where XX is the case number. All images are CT scans, under a wide variety of imaging conditions including high-dose and low-dose, with and without contrast, abdominal, neck-to-pelvis and whole-body. Many patients exhibit cancer lesions, especially in the liver, but they were not selected according to any specific disease criteria. Numeric values are in Hounsfield units.
Segmentations are stored as 'labels-XX.nii.gz', where XX is the same number as the corresponding volume file. Organs are encoded as follows:
0: Background (None of the following organs)
1: Liver 2: Bladder 3: Lungs 4: Kidneys 5: Bone 6: Brain TEST AND TRAIN SPLITS All organ masks were generated either (A) semi-automatically using ITK-SNAP, or (B) automatically using morphological algorithms. ITK-SNAP is a popular open-source program for medical image segmenation. Semi-automatic segmentation consists of manual editing with the 3D paintbrush tool, followed by refinement with active contours.
The first 21 volumes (case numbers 0-20) constitute the TESTING split. All organs in these volumes have been labeled with method (B). Bones were first labeled with method (A), then the result was refined with method (B). The remaining volumes constitute the TRAINING split. For these volumes, both lungs and bones were labeled with method (B). These masks suffice for training a deep neural network, but should not be considered reliable for evaluation. All other organs were labeled with method (A) for both the training and testing splits. For these organs, there is no difference in label accuracy between the two splits.
CREDITS These data were annotated between 2018-2019 by: -Blaine Rister -Kaushik Shivakumar 131 of the original images came from the Liver Tumor Segmentation Challenge (LiTS). Please see the challenge website (https://competitions.codalab.org/competitions/17094) for the credits for these images. Most of the liver masks for these images came from this challenge, although some were annotated by the above. 9 additional images were added from PET-CT patients from Stanford Healthcare, so that this additional imaging modality could be represented in the training and evaluation data.
Please direct questions to Blaine Rister by email at blaine@stanford.edu .
CITATIONS Please refer to the following paper to cite this data: - Arxiv [1901.04056] The Liver Tumor Segmentation Benchmark (LiTS) (https://arxiv.org/abs/1901.04056) |