Comparison of publicly available artificial intelligence models for pancreatic segmentation on T1-weighted Dixon images
PanSegNet demonstrated the highest segmentation accuracy and the best agreement with human measurements for both pancreatic volume and IPFF on T1-weighted Dixon images. This model appears to be the most suitable for large-scale studies requiring automated pancreatic segmentation and intrapancreatic fat evaluation.
Authors
- Osamu Abe (ORCID: https://orcid.org/0000-0002-1180-2629)
- Shota Fujisawa
- T. Yoshikawa (ORCID: https://orcid.org/0000-0002-1924-5468)
- Mariko Kurokawa (ORCID: https://orcid.org/0000-0002-3907-9188)
- Wataru Gonoi (ORCID: https://orcid.org/0000-0002-5018-4683)
- Shouhei Hanaoka (ORCID: https://orcid.org/0000-0002-7496-1651)
- Yuki Sonoda (ORCID: https://orcid.org/0009-0005-8622-2433)
Institutions
- University of Tokyo Hospital (JP)
- The University of Tokyo (JP)
Publication Details
- Journal
- Japanese Journal of Radiology
- Published
- 2025-06-18
- DOI
- https://doi.org/10.1007/s11604-025-01814-5
- Primary Topic
- Pancreatic and Hepatic Oncology Research
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- University of Tokyo