Dataset for gastrointestinal tract segmentation on serial MRIs for abdominal tumor radiotherapy
While manual segmentations are subject to bias and inter-observer variability, the dataset has been used for the UW-Madison GI Tract Image Segmentation Challenge hosted by Kaggle and may be used for ongoing segmentation algorithm development and potentially for dose accumulation algorithms.
Authors
- S.J. Olson
- Dustin Hebel
- Sangjune Laurence Lee (ORCID: https://orcid.org/0000-0002-1280-8109)
- John E. Bayouth (ORCID: https://orcid.org/0000-0001-8547-9716)
- Poonam Yadav (ORCID: https://orcid.org/0000-0003-0558-9646)
- Kailee Borchert
- Brianne Loritz
- M. Bassetti (ORCID: https://orcid.org/0000-0001-5012-8596)
- J.J. Meudt
- Jennifer T. Smilowitz (ORCID: https://orcid.org/0000-0003-1165-1944)
- Laila A. Gharzai (ORCID: https://orcid.org/0000-0001-6420-9274)
- Shervin Karimpour
- Alyx Alfson
- Yin Li
- Tera Kruser
- Jessica Strang
Institutions
- University of Wisconsin–Madison (US)
- Northwestern Memorial Hospital (US)
- Alberta Cancer Foundation (CA)
Publication Details
- Journal
- Data in Brief
- Published
- 2024-11-26
- DOI
- https://doi.org/10.1016/j.dib.2024.111159
- Primary Topic
- Radiomics and Machine Learning in Medical Imaging
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- University of Wisconsin-Madison
- University of Wisconsin Carbone Cancer Center