〔Abstract〕 Objective A semi-automatic threshold segmentation method was proposed in order to precisely segment patchy shadows of increased density, providing assistance for quantitative evaluation and statistical analysis of lesions. Methods From April 2019 to February 2020, 200 slices from 26 cases with viral pneumonia were recruited for analysis. Python platform was employed for pulmonary parenchyma extraction, local-region histogram analysis and adaptive thresholds designation. A total of 200 slices that contained lesions were delineated twice by two experienced radiologists, respectively. The interpersonal IOU and manmachine IOU were calculated for comparative study, the reliability of proposed method was validated consequently. Results The global mean man-machine IOU was 0.81(95 % confi dence interval: 0.78, 0.84), the global mean interpersonal IOU was 0.54(95 % confi dence interval: 0.51, 0.58). Conclusion Precise lesions can be precisely extracted by proposed semi-automatic segmentation method, laying foundation for precise and personalized treatment.