Summary
Patients were included based on the presence of lesions in one or more of the labeled organs. Most of the images exhibit liver lesions, both benign and malignant. Some also exhibit metastatic disease in other organs such as bones and lungs.
The images come from a wide variety of sources, including abdominal and full-body; contrast and non-contrast; low-dose and high-dose CT scans. 130 images are dedicated CTs, the remaining 10 are the CT component taken from PET-CT exams. This makes the dataset ideal for training and evaluating organ segmentation algorithms, which ought to perform well in a wide variety of imaging conditions.
The dataset is divided into a training set consisting of 130 CT scans, and a testing set constisting of the remaining 10. For the training set, the lungs and bones were automatically segmented by morphological image processing algorithms. The source code for these algorithms will be made publicly available. For the testing set, the lungs and bones were segmented manually by a human reader. All other organs were segmented manually in both the training and testing sets. Manual segmentations were done with ITK-SNAP, starting with semi-automatic active contour segmentation followed by manual clean-up.
Deep learning has the potential to make enormous advances in medical imaging analysis, but training these models requires large, diverse, painstakingly-annotated datasets. With 140 CT scans from a variety of sources, our dataset would be one of the largest of its kind in TCIA. To our knowledge, ours is the only one to include annotations of 5 different organ classes. Our dataset includes large and easily-located organs such as the lungs, as well as small and difficult ones like the bladder. We hope the dataset will enable widespread adoption of multi-class organ segmentation and competitive benchmarking of various computational approaches.
The creators used the dataset to successfully train a CT organ segmenter which is in active use in research projects at Stanford University.
Acknowledgements
- This work was supported in part by grants from the National Cancer Institute, National Institutes of Health, 1U01CA190214 and 1U01CA187947.
Data Access
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Detailed Description
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Modalities | NIfTI CT and segmentations |
Number of Patients | 140 |
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CTs and segmentations are saved in Nifti-1 (.nii) format. Each Nifti-1 file stores the entire CT volume in Hounsfield units. Segmentations are in patient-native space (no change in registration).
Source code will be provided as a link to Github, in Matlab and C languages.
Citations & Data Usage Policy
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These collections are freely available to browse, download, and use for commercial, scientific and educational purposes as outlined in the Creative Commons Attribution 3.0 Unported License. Questions may be directed to help@cancerimagingarchive.net. Please be sure to acknowledge both this data set and TCIA in publications by including the following citations in your work:
Data Citation
Blaine Rister, Kaushik Shivakumar, Tomomi Nobashi and Daniel L. Rubin. (2019)
Acknowledgement
- 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
TCIA Citation
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
Other Publications Using This Data
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Version X (Current): Updated yyyy/mm/dd
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Added new subjects.
Version 1: Updated 2018/10/24
Added new subjects.