Notice: Function Pods Templates was called incorrectly. Pod Template PHP code is no longer actively supported and will be completely removed in Pods 3.3 Please see Debugging in WordPress for more information. (This message was added in version 3.0.) in /var/www/html/wp-includes/functions.php on line 6085
Deprecated: Pod Template PHP code has been deprecated, please use WP Templates instead of embedding PHP. has been deprecated since Pods version 2.3 with no alternative available. in /var/www/html/wp-content/plugins/pods/includes/general.php on line 1037
Pancreas-CT | Pancreas-CT
DOI: 10.7937/K9/TCIA.2016.tNB1kqBU | Data Citation Required | Image Collection
| Location | Species | Subjects | Data Types | Cancer Types | Size | Status | Updated | |
|---|---|---|---|---|---|---|---|---|
| Pancreas | Human | 82 | CT, Segmentation | Healthy Controls (non-cancer) | Image Analyses | Public, Complete | 2020/09/10 |
Summary
The National Institutes of Health Clinical Center performed 82 abdominal contrast enhanced 3D CT scans (~70 seconds after intravenous contrast injection in portal-venous) from 53 male and 27 female subjects. Seventeen of the subjects are healthy kidney donors scanned prior to nephrectomy. The remaining 65 patients were selected by a radiologist from patients who neither had major abdominal pathologies nor pancreatic cancer lesions. Subjects' ages range from 18 to 76 years with a mean age of 46.8 ± 16.7. The CT scans have resolutions of 512x512 pixels with varying pixel sizes and slice thickness between 1.5 − 2.5 mm, acquired on Philips and Siemens MDCT scanners (120 kVp tube voltage). A medical student manually performed slice-by-slice segmentations of the pancreas as ground-truth and these were verified/modified by an experienced radiologist.
Data Access
Version 2: Updated 2020/09/10
Note: Previously posted cases #25 and #70 were found to be from the same scan as case #2, just cropped slightly differently, and were removed from this version of the dataset.
| Title | Data Type | Format | Access Points | Subjects | License | |||
|---|---|---|---|---|---|---|---|---|
| Images | CT | DICOM | Download requires NBIA Data Retriever |
80 | 80 | 80 | 18,942 | CC BY 3.0 |
| Manual Annotations | Segmentation | ZIP and NIFTI | 80 | 80 | CC BY 3.0 |
Additional Resources for this Dataset
The NCI Cancer Research Data Commons (CRDC) provides access to additional data and a cloud-based data science infrastructure that connects data sets with analytics tools to allow users to share, integrate, analyze, and visualize cancer research data.
- Imaging Data Commons (IDC) (Imaging Data)
- IDC Zenodo community datasets:
Citations & Data Usage Policy
Data Citation Required: Users must abide by the TCIA Data Usage Policy and Restrictions. Attribution must include the following citation, including the Digital Object Identifier:
Data Citation |
|
|
Roth, H., Farag, A., Turkbey, E. B., Lu, L., Liu, J., & Summers, R. M. (2016). Data From Pancreas-CT (Version 2) [Data set]. The Cancer Imaging Archive. https://doi.org/10.7937/K9/TCIA.2016.tNB1kqBU |
Detailed Description
Data Example
Note
The DICOM files were created from anonymized volumetric images (Analyze and NifTI) using this from ITK: http://www.itk.org/Doxygen/html/Examples_2IO_2ImageReadDicomSeriesWrite_8cxx-example.html .
Related Publications
Publications by the Dataset Authors
The authors recommended this paper as the best source of additional information about this dataset:
Roth HR, Lu L, Farag A, Shin H-C, Liu J, Turkbey EB, Summers RM. DeepOrgan: Multi-level Deep Convolutional Networks for Automated Pancreas Segmentation. N. Navab et al. (Eds.): MICCAI 2015, Part I, LNCS 9349, pp. 556–564, 2015. (arXiv link) https://doi.org/10.1007/978-3-319-24553-9_68
No publications by dataset authors were found.
Research Community Publications
TCIA maintains a list of publications that leverage TCIA data. If you have a manuscript you’d like to add please contact the TCIA Helpdesk. Below is a list of such publications using this Collection:
- Gibson, E., Giganti, F., Hu, Y., Bonmati, E., Bandula, S., Gurusamy, K., . . . Barratt, D. C. (2017). Towards Image-Guided Pancreas and Biliary Endoscopy: Automatic Multi-organ Segmentation on Abdominal CT with Dense Dilated Networks. Paper presented at the International Conference on Medical Image Computing and Computer-Assisted Intervention.
- Greenspan, H., van Ginneken, B., & Summers, R. M. (2016). Guest Editorial Deep Learning in Medical Imaging: Overview and Future Promise of an Exciting New Technique. IEEE Transactions on Medical Imaging, 35(5), 1153-1159. doi:10.1109/TMI.2016.2553401
- Shi, H., Lu, L., Yin, M., Zhong, C., & Yang, F. (2023). Joint few-shot registration and segmentation self-training of 3D medical images. Biomedical Signal Processing and Control, 80. doi:https://doi.org/10.1016/j.bspc.2022.104294