Effects of data-centric multi-task learning with larger patch sizes on pulmonary nodule segmentation performance
10.3969/j.issn.1005-202X.2025.10.007
- VernacularTitle:基于以数据为中心的多任务学习及切片尺寸对肺结节分割性能的影响
- Author:
Jian LIU
1
;
Zheng ZHANG
;
Bing NIU
;
Shuai KANG
;
Juan REN
;
Lei WANG
;
Kai XU
Author Information
1. 上海交通大学机械与动力工程学院,上海 200241
- Publication Type:Journal Article
- Keywords:
pulmonary nodule;
multi-task learning;
deep learning;
segmentation performance
- From:
Chinese Journal of Medical Physics
2025;42(10):1306-1320
- CountryChina
- Language:Chinese
-
Abstract:
Given the lack of annotations for key lung organs and tissues in existing public datasets,this study collected 863 cases of chest CT scan images and constructed the first comprehensive dataset containing annotations of pulmonary vessels,airways,and nodules using a semi-automated method that combines computer vision algorithms with manual corrections by radiologists.On this basis,a lung nodule segmentation model based on multi-task learning is proposed.By incorporating annotations of pulmonary vessels(pulmonary arteries and veins)and the trachea to enhance model's ability to learn lung features,the proposed model reduces the false discovery rate in lung nodule detection,and improves generalization ability.Additionally,the use of larger image patches further optimizes model performance.The trained VAAN_128 model achieves the best performance,with a Dice coefficient of 0.694 and a false discovery rate of 0.210 for lung nodule segmentation.Moreover,it simultaneously provides accurate segmentation results of pulmonary vessels and the trachea,assisting in the formulation of more precise diagnosis and treatment plans.Based on the VAAN_128 model,a software system for navigation and localization in biopsy procedures is developed.In clinical practice,this system can assist physicians in accurately locating lung nodules,distinguishing critical tissues,and improving preoperative planning efficiency.This provides precise and efficient technical support for early diagnosis and disease monitoring of lung diseases,and is of great significance for path planning in clinical navigation system and future lung imaging research.
