Assessing quantitative performance and expert review of multiple deep learning-based frameworks for computed tomography-based abdominal organ auto-segmentation
The findings suggest that AutoML frameworks offer a significant advantage in the segmentation of abdominal organs, and underscores the potential of AutoML methods to enhance the efficiency of oncological workflows.
Authors
- Joel A. Pogue (ORCID: https://orcid.org/0000-0003-4450-6698)
- Rojymon Jacob (ORCID: https://orcid.org/0000-0003-2912-8422)
- M. Soike (ORCID: https://orcid.org/0000-0002-8444-3241)
- Carlos E. Cardenas (ORCID: https://orcid.org/0000-0003-1414-3849)
- Neil T. Pfister (ORCID: https://orcid.org/0000-0001-7369-5710)
- Udbhav S Ram (ORCID: https://orcid.org/0000-0002-0457-7259)
Institutions
- University of Alabama at Birmingham (US)
Publication Details
- Journal
- Intelligent Oncology
- Published
- 2025-03-19
- DOI
- https://doi.org/10.1016/j.intonc.2025.03.003
- Citations
- 3
- Primary Topic
- Radiomics and Machine Learning in Medical Imaging
- Type
- article
- Field-Weighted Citation Impact
- 4.08
Funders
- National Science Foundation
- Indiana University
- University of Alabama
- National Institutes of Health
- National Cancer Institute
- National Center for Advancing Translational Sciences