Automated abdominal organ segmentation algorithms for non-enhanced CT for volumetry and 3D radiomics analysis
Our nnU-Net-based models accurately segmented abdominal solid organs in non-enhanced abdominal and low-dose chest CT, enabling reliable automated measurements of organ volume and specific 3D radiomics features.
Authors
- Sun Kyung Jeon (ORCID: https://orcid.org/0000-0002-8991-3986)
- Ijin Joo (ORCID: https://orcid.org/0000-0002-1341-4072)
- Junghoan Park (ORCID: https://orcid.org/0000-0002-0636-3756)
- Soon Ho Yoon (ORCID: https://orcid.org/0000-0002-3700-0165)
- Jongmin Kim (ORCID: https://orcid.org/0000-0002-6622-3368)
- Sang Joon Park (ORCID: https://orcid.org/0000-0003-1013-681X)
Institutions
- Seoul National University (KR)
- Seoul National University Hospital (KR)
- Seoul Medical Center (KR)
Publication Details
- Journal
- Abdominal Radiology
- Published
- 2024-09-19
- DOI
- https://doi.org/10.1007/s00261-024-04581-5
- Citations
- 9
- Primary Topic
- Radiomics and Machine Learning in Medical Imaging
- Type
- article
- Field-Weighted Citation Impact
- 3.64
Funders
- National Research Foundation
- Seoul National University
- National Research Foundation of Korea
- Seoul National University Hospital
- Ministry of Science and ICT, South Korea