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Updated: May 12, 2026
Three-dimensional Location Approach with Silk Thread Guided Laparoscopic Segmentectomy for Liver Tumor
Published on: May 23, 2025
Computer-aided focal liver lesion detection
Yanling Chi1, Jiayin Zhou, Sudhakar K Venkatesh
1Singapore Bioimaging Consortium, Agency for Science, Technology and Research, 30 Biopolis Street, #07-01, Matrix, 138671 , Singapore, Singapore. yanling.chi@gmail.com
Purpose:
Our aim is to develop an automatic method which can detect diverse focal liver lesions (FLLs) in 3D CT volumes.
Method:
A hybrid generative-discriminative framework is proposed. It first uses a generative model to describe non-lesion components and then identifies all candidate FLLs within a 3D liver volume by eliminating non-lesion components. It subsequently uses a discriminative approach to suppress false positives with the advantage of tumoroid, a novel measurement combining three shape features spherical symmetry, compactness and size.
Results:
This method was tested on 71 abdominal CT datasets (5,854 slices from 61 patients, with 261 FLLs covering six pathological types) and evaluated using the free-response receiver operating characteristic (FROC) curves. Overall, it achieved a true positive rate of 90 % with one false positive per liver. It degenerated gently with the decrease in lesion sizes to 30 ml. It achieved a true-positive rate of 36 % when tested on the lesions less than 4 ml. The average computing time of the lesion detection is 4 min and 28 s per CT volume on a PC with 2.67 GHz CPU and 4.0 GB RAM.
Conclusions:
The proposed method is comparable to the radiologists' visual investigation in terms of efficiency. The tool has great potential to reduce radiologists' burden in going through thousands of images routinely.
