CT-ORG: CT volumes with multiple organ segmentations

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 segmentation. 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 were borrowed from the Liver Tumor Segmentation (LiTS) challenge, which the organizers have generously allowed us to distribute. For more information, see the following website and paper:
- https://lits-challenge.com
- Arxiv [1901.04056] The Liver Tumor Segmentation Benchmark (LiTS) (https://arxiv.org/abs/1901.04056)

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 also came from LiTS, although some were annotated by the above. Additional CT 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)

Blaine Rister, Kaushik Shivakumar, Tomomi Nobashi and Daniel L. Rubin. (2019)

1. CT organ segmentation using GPU data augmentation, unsupervised labels and IOU loss. Blaine Rister, Darvin Yi, Kaushik Shivakumar, Tomomi Nobashi and Daniel L. Rubin. https://arxiv.org/abs/1811.11226
2. Arxiv [1901.04056] The Liver Tumor Segmentation Benchmark (LiTS) https://arxiv.org/abs/1901.04056

Clark K, Vendt B, Smith K, Freymann J, Kirby J, Koppel P, Moore S, Phillips S, Maffitt D, Pringle M, Tarbox L, Prior F. The Cancer Imaging Archive (TCIA): Maintaining and Operating a Public Information Repository, Journal of Digital Imaging, Volume 26, Number 6, December, 2013, pp 1045-1057. DOI: 10.1007/s10278-013-9622-7