Training robust T1-weighted magnetic resonance imaging liver segmentation models using ensembles of datasets with different contrast protocols and liver disease etiologies
Image segmentation of the liver is an important step in treatment planning for liver cancer. However, manual segmentation at a large scale is not practical, leading to increasing reliance on deep learning models to automatically segment the liver. This manuscript develops a generalizable deep learning model to segment the liver on T1-weighted MR images. In particular, three distinct deep learning architectures (nnUNet, PocketNet, Swin UNETR) were considered using data gathered from six geographically different institutions. A total of 819 T1-weighted MR images were gathered from both public and internal sources. Our experiments compared each architecture's testing performance when trained both intra-institutionally and inter-institutionally. Models trained using nnUNet and its PocketNet variant achieved mean Dice-Sorensen similarity coefficients>0.9 on both intra- and inter-institutional test set data. The performance of these models suggests that nnUNet and PocketNet liver segmentation models trained on a large and diverse collection of T1-weighted MR images would on average achieve good intra-institutional segmentation performance.
Authors
- Mohamed Eltaher (ORCID: https://orcid.org/0009-0008-2078-4718)
- Kristy K. Brock (ORCID: https://orcid.org/0000-0001-9364-5040)
- Tiffany L. Calderone (ORCID: https://orcid.org/0000-0003-4404-5342)
- Eugene J. Koay (ORCID: https://orcid.org/0000-0001-7675-3461)
- Matthew Cagley (ORCID: https://orcid.org/0000-0002-1595-1699)
- David Fuentes (ORCID: https://orcid.org/0000-0002-2572-6962)
- Nihil Patel (ORCID: https://orcid.org/0009-0008-8368-8512)
- David W. Victor (ORCID: https://orcid.org/0000-0003-1414-3128)
- Adrian Celaya (ORCID: https://orcid.org/0000-0003-0041-7239)
- Ahmed Elsaiey
- Nakul Gupta (ORCID: https://orcid.org/0000-0002-2413-2193)
- Kari J. Brewer Savannah (ORCID: https://orcid.org/0000-0002-8908-7004)
- Rachel Glenn (ORCID: https://orcid.org/0000-0003-1238-4905)
- Laura Beretta (ORCID: https://orcid.org/0000-0002-2054-684X)
- Tucker Netherton (ORCID: https://orcid.org/0000-0003-1583-7121)
- Darrel Cleere
- Jessica I. Sanchez
Institutions
- The University of Texas MD Anderson Cancer Center (US)
- Methodist Hospital (US)
- Rice University (US)
Publication Details
- Journal
- Scientific Reports
- Published
- 2024-09-09
- DOI
- https://doi.org/10.1038/s41598-024-71674-y
- Citations
- 7
- Primary Topic
- Radiomics and Machine Learning in Medical Imaging
- Type
- article
- Field-Weighted Citation Impact
- 2.83
Funders
- National Science Foundation
- U.S. Department of Defense
- National Institutes of Health
- University of Texas MD Anderson Cancer Center
- National Cancer Institute