Beyond radiologist-level liver lesion detection on multi-phase contrast-enhanced CT images by deep learning

作者：Lei Wu, Haishuai Wang, Yining Chen, Xiang Zhang, Tianyun Zhang, Ning Shen, Guangyu Tao, Zhongquan Sun, Yuan Ding, Weilin Wang, Jiajun Bu · 发表于：iScience · 年份：2023 · DOI：10.1016/j.isci.2023.108183 · 被引用次数：20 · 研究领域：Advanced X-ray and CT Imaging、Radiomics and Machine Learning in Medical Imaging、Brain Tumor Detection and Classification

Accurate detection of liver lesions from multi-phase contrast-enhanced CT (CECT) scans is a fundamental step for precise liver diagnosis and treatment. However, the analysis of multi-phase contexts is heavily challenged by the misalignment caused by respiration coupled with the movement of organs. Here, we proposed an AI system for multi-phase liver lesion segmentation (named MULLET) for precise and fully automatic segmentation of real-patient CECT images. MULLET enables effectively embedding the important ROIs of CECT images and exploring multi-phase contexts by introducing a transformer-based attention mechanism. Evaluated on 1,229 CECT scans from 1,197 patients, MULLET demonstrated significant performance gains in terms of Dice, Recall, and F2 score, which are 5.80%, 6.57%, and 5.87% higher than state of the arts, respectively. MULLET has been successfully deployed in real-world settings. The deployed AI web server provides a powerful system to boost clinical workflows of liver lesion diagnosis and could be straightforwardly extended to general CECT analyses.