Liver cancer segmentator: Metadata-guided confidence scoring for reliable segmentation of colorectal liver metastases in CT

BACKGROUND AND OBJECTIVE: This study introduces the liver cancer segmentator (LCS), a deep learning model designed for automatic and robust segmentation of liver parenchyma and tumors in abdominal contrast-enhanced computed tomography images from patients with colorectal liver metastases. The primary aim was to enhance confidence scoring for more reliable clinical segmentation assessment. METHODS: In this retrospective study, 446 abdominal contrast-enhanced computed tomography examinations were collected; 355 (80%) were used for training and 91 for testing. Data originated from routine clinical cases at two institutions, representing diverse disease stages and treatment settings. A state-of-the-art neural network segmentation framework was trained on these cases, with performance evaluated using the Dice score and the normalized surface distance. An iterative training process, supported by an integrated annotation workflow, was employed to refine the training set. The final model was applied to the 91 test examinations to assess the impact of tumor volume and slice thickness on confidence scoring. Reliability was quantified through pairwise Dice score for failure detection and the area under the risk coverage curve. RESULTS: The LCS achieved a Dice score of 0.9707 (95% CI: 0.9663-0.9751) for liver parenchyma and 0.7695 (95% CI: 0.7166-0.8224) for tumors. Normalized surface distance values at a 3-millimeter tolerance were 0.9605 (95% CI: 0.9539-0.9671) for parenchyma and 0.8412 (95% CI: 0.7928-0.8896) for tumors. Confidence scoring analysis demonstrated strong correlations between tumor volume, slice thickness, and segmentation reliability, reducing the area under the risk coverage curve from 16.7 to 10.3. CONCLUSIONS: 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.