Precise ablation zone segmentation on CT images after liver cancer ablation using semi‐automatic CNN‐based segmentation
The proposed semi-automatic CNN-based segmentation method can be used to effectively segment the ablation zones, increasing the value of CECT for an assessment of treatment success. For reproducibility, the trained models, source code, and demonstration tool are publicly available at https://github.com/lqanh11/Interactive_AblationZone_Segmentation.
Authors
- Quoc Anh Le
- Theo van Walsum (ORCID: https://orcid.org/0000-0001-8257-7759)
- Xuan Loc Pham (ORCID: https://orcid.org/0000-0001-7651-2887)
- Daniel Franklin (ORCID: https://orcid.org/0000-0002-9563-5943)
- Adriaan Moelker (ORCID: https://orcid.org/0000-0002-9503-788X)
- Nguyen Linh Trung (ORCID: https://orcid.org/0000-0002-3103-994X)
- Ha Vu Le (ORCID: https://orcid.org/0000-0003-3441-1409)
- Tuan Linh Le
- Hang Viet Dao (ORCID: https://orcid.org/0000-0002-3685-9496)
- Ha Manh Luu (ORCID: https://orcid.org/0000-0002-5678-6212)
Institutions
- University of Technology Sydney (AU)
- Hanoi Medical University (VN)
- Erasmus MC (NL)
- Hanoi Medical University Hospital (VN)
- VNU University of Science (VN)
- Erasmus University Rotterdam (NL)
Publication Details
- Journal
- Medical Physics
- Published
- 2024-09-09
- DOI
- https://doi.org/10.1002/mp.17373
- Citations
- 5
- Primary Topic
- Radiomics and Machine Learning in Medical Imaging
- Type
- article
- Field-Weighted Citation Impact
- 2.02
Funders
- National Foundation for Science and Technology Development