Versions Compared
Key
- This line was added.
- This line was removed.
- Formatting was changed.
Summary
This dataset consists of 140 computed tomography (CT) scans, each with five organs labeled in 3D: lung, bones, liver, kidneys and bladder. The brain is also labeled on the minority of scans which show it.
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 isdataset 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, as well as competitive benchmarking of algorithms for it.
The data are divided into a
trainingtesting set
consistingof
13021 CT scans, and a
testingtraining set
constistingof the remaining
10119. 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 (https://www.itksnap.org), 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.
The source code for the morphological algorithms is available at:
- https://github.com/bbrister/ctOrganSegmentation.git
Many 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)
Acknowledgements
- This work was supported in part by grants from the National Cancer Institute, National Institutes of Health, 1U01CA190214 and 1U01CA187947.
Localtab Group | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
|