Deep learning for accurate lung artery segmentation with shape-position priors
- VernacularTitle:基于深度学习和形状位置先验的肺动脉形状分割方法
- Author:
Chao GUO
1
;
Xuehan GAO
1
;
Qidi HU
2
;
Jian LI
3
;
Haixing ZHU
3
;
Ke ZHAO
1
;
Weipeng LIU
3
;
Shanqing LI
1
Author Information
1. Department of Thoracic Surgery, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100730, P. R. China
2. 2. Hebei University of Technology, School of Artificial Intelligence and Data Science, Tianjin, 300401, P. R. China 3. Hubei Industrial Internet Industry Technology Research Institute, Huangshi, 431602, Hubei, P. R. China
3. Hebei University of Technology, School of Artificial Intelligence and Data Science, Tianjin, 300401, P. R. China
- Publication Type:Journal Article
- Keywords:
Pulmonary artery segmentation;
deep learning;
three-dimensional U-Net;
prior knowledge;
surgical navigation
- From:
Chinese Journal of Clinical Thoracic and Cardiovascular Surgery
2025;32(03):332-338
- CountryChina
- Language:Chinese
-
Abstract:
Objective To propose a lung artery segmentation method that integrates shape and position prior knowledge, aiming to solve the issues of inaccurate segmentation caused by the high similarity and small size differences between the lung arteries and surrounding tissues in CT images. Methods Based on the three-dimensional U-Net network architecture and relying on the PARSE 2022 database image data, shape and position prior knowledge was introduced to design feature extraction and fusion strategies to enhance the ability of lung artery segmentation. The data of the patients were divided into three groups: a training set, a validation set, and a test set. The performance metrics for evaluating the model included Dice Similarity Coefficient (DSC), sensitivity, accuracy, and Hausdorff distance (HD95). Results The study included lung artery imaging data from 203 patients, including 100 patients in the training set, 30 patients in the validation set, and 73 patients in the test set. Through the backbone network, a rough segmentation of the lung arteries was performed to obtain a complete vascular structure; the branch network integrating shape and position information was used to extract features of small pulmonary arteries, reducing interference from the pulmonary artery trunk and left and right pulmonary arteries. Experimental results showed that the segmentation model based on shape and position prior knowledge had a higher DSC (82.81%±3.20% vs. 80.47%±3.17% vs. 80.36%±3.43%), sensitivity (85.30%±8.04% vs. 80.95%±6.89% vs. 82.82%±7.29%), and accuracy (81.63%±7.53% vs. 81.19%±8.35% vs. 79.36%±8.98%) compared to traditional three-dimensional U-Net and V-Net methods. HD95 could reach (9.52±4.29) mm, which was 6.05 mm shorter than traditional methods, showing excellent performance in segmentation boundaries. Conclusion The lung artery segmentation method based on shape and position prior knowledge can achieve precise segmentation of lung artery vessels and has potential application value in tasks such as bronchoscopy or percutaneous puncture surgery navigation.
