Development and validation of AI-based automatic segmentation and measurement of thymus on chest CT scans.
Authors
Affiliations (11)
Affiliations (11)
- Department of Radiology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, No.1277 Jiefang Avenue, Wuhan, 430022, China.
- Hubei Provincial Clinical Research Center for Precision Radiology & Interventional Medicine, Wuhan, 430022, China.
- Hubei Key Laboratory of Molecular Imaging, Wuhan, 430022, China.
- Beijing Wandong Medical Technology Co.,Ltd, Beijing, 100015, China.
- Department of Radiology, The Key Laboratory of Advanced Interdisciplinary Studies Center, National Center for Respiratory Medicine, The First Affiliated Hospital of Guangzhou Medical University, Guangzhou, 510120, China.
- Department of Radiology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, No.1277 Jiefang Avenue, Wuhan, 430022, China. [email protected].
- Hubei Provincial Clinical Research Center for Precision Radiology & Interventional Medicine, Wuhan, 430022, China. [email protected].
- Hubei Key Laboratory of Molecular Imaging, Wuhan, 430022, China. [email protected].
- Department of Radiology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, No.1277 Jiefang Avenue, Wuhan, 430022, China. [email protected].
- Hubei Provincial Clinical Research Center for Precision Radiology & Interventional Medicine, Wuhan, 430022, China. [email protected].
- Hubei Key Laboratory of Molecular Imaging, Wuhan, 430022, China. [email protected].
Abstract
Due to the complex anatomical structure and dynamic involution process of the thymus, segmentation and evaluation of the thymus in medical imaging present significant challenges. The aim of this study is to develop a deep-learning tool "Thy-uNET" for automatic segmentation and measurement of the thymus or thymic region on chest CT imaging, and to validate its performance with multicenter data. Utilizing the segmentation and measurement results from two experts, training of Thy-uNET was conducted on training cohort (n = 500). The segmented regions include thymus or thymic region, and 7 features of the thymic region were measured. The automatic segmentation performance was assessed using Dice and Intersection over Union (IOU) on CT data from three test cohorts (n = 286). Spearman correlation analysis and intraclass correlation coefficient (ICC) were used to evaluate the correlation and reliability of the automatic measurement results. Six radiologists with varying levels of experience were invited to participate in a reader study to assess the measurement performance of Thy-uNET and its ability to assist doctors. Thy-uNET demonstrated consistent segmentation performance across different subgroups, with Dice = 0.83 in the internal test set, and Dice = 0.82 in the external test sets. For automatic measurement of thymic features, Thy-uNET achieved high correlation coefficients and ICC for key measurements (R = 0.829 and ICC = 0.841 for CT attenuation measurement). Its performance was comparable to that of radiology residents and junior radiologists, with significantly shorter measurement time. Providing Thy-uNET measurements to readers reduced their measurement time and improved residents' performance in some thymic feature measurements. Thy-uNET can provide reliable automatic segmentation and automatic measurement information of the thymus or thymic region on routine CT, reducing time costs and improving the consistency of evaluations.