Quantitative analysis of myocardial fibrosis in dilated cardiomyopathy with deep learning joint segmentation model
10.3760/cma.j.cn112149-20220701-00560
- VernacularTitle:基于深度学习联合分割模型对扩张型心肌病心肌纤维化的定量分析
- Author:
Nannan YU
1
;
Dan XU
;
Chun′ai HU
;
Lina DOU
;
Jupan HOU
;
Jingxi SUN
;
Bing HAN
Author Information
1. 江苏师范大学电气工程及自动化学院，徐州 221116
- Keywords:
Artificial intelligence;
Cardiomyopathy,dilated;
Deep learning;
Joint segmentation model;
Myocardial fibrosis
- From:
Chinese Journal of Radiology
2023;57(5):522-527
- CountryChina
- Language:Chinese
-
Abstract:
Objective:To explore the effect of joint segmentation model of myocardial-fibrotic region based on deep learning in quantitative analysis of myocardial fibrosis in patients with dilated cardiomyopathy(DCM).Methods:The data of 200 patients with confirmed DCM and myocardial fibrosis in the left ventricle detected by cardiac MR-late gadolinium enhancement (CMR-LGE) in Xuzhou Central Hospital from January 2015 to April 2022 were retrospectively analyzed. Using a complete randomized design, the patients were divided into training set ( n=120), validation set ( n=30) and test set ( n=50). The left ventricle myocardium was outlined and the normal myocardial region was selected by radiologists. Fibrotic myocardium was extracted through calculating the threshold with standard deviation (SD) as a reference standard for left ventricle segmentation and fibrosis quantification. The left ventricular myocardium was segmented by convex prior U-Net network. Then the normal myocardial image block was recognized by VGG image classification network, and the fibrosis myocardium was extracted by SD threshold. The myocardial segmentation effect was evaluated using precision, recall, intersection over union (IOU) and Dice coefficient. The consistency of myocardial fibrosis ratio in left ventricle obtained by joint segmentation model and manual extraction was evaluated with intra-class correlation coefficient (ICC). According to the median of fibrosis rate, the samples were divided into mild and severe fibrosis, and the quantitative effect of fibrosis was compared by Mann-Whitney U test. Results:In the test set, the precision of myocardial segmentation was 0.827 (0.799, 0.854), the recall was 0.849 (0.822, 0.876), the IOU was 0.788 (0.760, 0.816), and the Dice coefficient was 0.832 (0.807, 0.857). The consistency of fibrosis ratio between joint segmentation model and manual extraction was high (ICC=0.991, P<0.001). No statistically significant difference was found in the ratio error between mild and severe fibrosis ( P>0.05). Conclusions:The joint segmentation model realizes the automatic calculation of myocardial fibrosis ratio in left ventricle, which is highly consistent with the results of manual extraction. Therefore, it can accurately realize the automatic quantitative analysis of myocardial fibrosis in patients with dilated cardiomyopathy.
