Comparative analysis of nnU-Net and Auto3Dseg for fat and fibroglandular tissue segmentation in MRI
Ensemble approaches of Auto3Dseg and nnU-Net demonstrated comparable performance in segmenting fat and FGT on breast MRI. The significant differences in fat HD underscore the importance of boundary-focused metrics in evaluating segmentation methods.
Authors
- Yasna Forghani
- Fátima Cardoso (ORCID: https://orcid.org/0000-0002-6692-2249)
- Mário A. T. Figueiredo (ORCID: https://orcid.org/0000-0002-0970-7745)
- Maria João Cardoso (ORCID: https://orcid.org/0000-0002-8137-3700)
- Pedro Gouveia (ORCID: https://orcid.org/0000-0001-5600-2783)
- Tiago Marques (ORCID: https://orcid.org/0000-0002-8973-0549)
- Nuno Loução (ORCID: https://orcid.org/0000-0003-1810-2817)
- João Santinha (ORCID: https://orcid.org/0000-0001-8174-2943)
- Rafaela Timóteo (ORCID: https://orcid.org/0000-0003-4369-2775)
Institutions
- Champalimaud Foundation (PT)
- University of Lisbon (PT)
- Universidade Politécnica de Lisboa (PT)
Publication Details
- Journal
- Journal of Medical Imaging
- Published
- 2025-04-16
- DOI
- https://doi.org/10.1117/1.jmi.12.2.024005
- Citations
- 2
- Primary Topic
- Radiomics and Machine Learning in Medical Imaging
- Type
- article
- Field-Weighted Citation Impact
- 2.72