AI-ready rectal cancer MR imaging: a workflow for tumor detection and segmentation
This study demonstrates the feasibility of generating high-quality labeled MR datasets through collaboration between a radiologist and two data scientists, which is essential for training AI models to automate tumor detection and segmentation in rectal cancer. By integrating expertise from radiology and data science, this approach has the potential to enhance AI model performance and transform clinical workflows in the future.
Authors
- Arden M. Morris (ORCID: https://orcid.org/0000-0001-5795-2940)
- Todd H. Wagner (ORCID: https://orcid.org/0000-0001-7625-3504)
- Vipul Sheth (ORCID: https://orcid.org/0000-0001-6130-4276)
- Erqi Liu Pollom (ORCID: https://orcid.org/0000-0003-2908-6293)
- Young‐Ik Son (ORCID: https://orcid.org/0000-0002-2114-8085)
- Heather M. Selby
Institutions
- Stanford University (US)
Publication Details
- Journal
- BMC Medical Imaging
- Published
- 2025-03-14
- DOI
- https://doi.org/10.1186/s12880-025-01614-3
- Citations
- 3
- Primary Topic
- Radiomics and Machine Learning in Medical Imaging
- Type
- article
- Field-Weighted Citation Impact
- 4.08