Liver cancer segmentator: Metadata-guided confidence scoring for reliable segmentation of colorectal liver metastases in CT
The LCS achieved high segmentation accuracy in patients with colorectal liver metastases. Incorporating tumor volume and slice thickness into the confidence scoring process improved failure detection, enhanced reliability, and provided valuable insights for refining clinical deployment of automated segmentation algorithms.
Authors
- Yun Shin Chun (ORCID: https://orcid.org/0000-0003-1384-8927)
- Mohammad Hamghalam (ORCID: https://orcid.org/0000-0003-2543-0712)
- Hyunseon C. Kang (ORCID: https://orcid.org/0000-0001-5313-3526)
- Jacob Peoples (ORCID: https://orcid.org/0000-0003-0191-7446)
- Natalie Gangai (ORCID: https://orcid.org/0000-0003-0603-7165)
- MITHAT GÖNEN
- Grace Park
- Richard ev k
- Erin Kwak
- Kaitlyn S.M. Kobayashi
- Amber L. Simpson
- E. Claire Bunker
Institutions
- Memorial Sloan Kettering Cancer Center (US)
- The University of Texas MD Anderson Cancer Center (US)
- Qazvin Islamic Azad University (IR)
- Queen's University (CA)
Publication Details
- Journal
- Computer Methods and Programs in Biomedicine
- Published
- 2026-01-03
- DOI
- https://doi.org/10.1016/j.cmpb.2026.109233
- Primary Topic
- Hepatocellular Carcinoma Treatment and Prognosis
- Type
- article
- Field-Weighted Citation Impact
- 0.00