An automatic segmentation method with self-attention mechanism on left ventricle in gated PET/CT myocardial perfusion imaging. (February 2023)

• Record Type: Journal Article
• Authors: Zhang, Yangmei; Wang, Fanghu; Wu, Huiqin; Yang, Yuling; Xu, Weiping; Wang, Shuxia; Chen, Wufan; Lu, Lijun
• Is Part Of: Computer methods and programs in biomedicine. Volume 229(2023)
• Publication Date: 2023-02
• Subjects: 13N-NH3 PET/CT -- Coronary artery disease -- Automatic segmentation -- Self-attention mechanism -- LV function assessment; Medicine -- Computer programs -- Periodicals; Biology -- Computer programs -- Periodicals; Computers -- Periodicals; Medicine -- Periodicals; Médecine -- Logiciels -- Périodiques; Biologie -- Logiciels -- Périodiques; Biology -- Computer programs; Medicine -- Computer programs; Periodicals; Electronic journals; 610.28
• DOI: 10.1016/j.cmpb.2022.107267
• Languages: English
• ISSNs: 0169-2607

Abstract

Highlights: We have developed a comprehensive radiomic analysis framework for the diagnosis of obstructive CAD. We found that radiomic phenotype has the potential to discriminate obstructive CAD. We validated that joint rest PET MPl and MMl has superior diagnostic performance compared with single imaging modality. Abstract: Objectives: We aimed to propose an automatic segmentation method for left ventricular (LV) from 16 electrocardiogram (ECG) -gated 13 N-NH3 PET/CT myocardial perfusion imaging (MPI) to improve the performance of LV function assessment. Methods: Ninety-six cases with confirmed or suspected obstructive coronary artery disease (CAD) were enrolled in this research. The LV myocardial contours were delineated by physicians as ground truth. We developed an automatic segmentation method, which introduces the self-attention mechanism into 3D U-Net to capture global information of images so as to achieve fine segmentation of LV. Three cross-validation tests were performed on each gate (64 vs. 32 for training vs. validation). The effectiveness was validated by quantitative metrics (modified hausdorff distance, MHD; dice ratio, DR; 3D MHD) as well as cardiac functional parameters (end-systolic volume, ESV; end-diastolic volume, EDV; ejection fraction, EF). Furthermore, the feasibility of the proposed method was also evaluated by intra- and inter-observers with DR and 3D-MHD. Results: Compared with backbone network, the proposed approach improved the average DR from 0.905 ± 0.0193 to 0.9202 ± 0.0164, and decreased the average 3D MHD from 0.4611 ± 0.0349 to 0.4304 ± 0.0339. The average relative error of LV volume between proposed method and ground truth is 1.09±3.66%, and the correlation coefficient is 0.992 ± 0.007 ( P < 0.001). The EDV, ESV, EF deduced from the proposed approach were highly correlated with ground truth ( r ≥ 0.864, P < 0.001), and the correlation with commercial software is fair ( r ≥ 0.871, P < 0.001). DR and 3D MHD of contours and myocardium from two observers are higher than 0.899 and less than 0.5194. Conclusion: The proposed approach is highly feasible for automatic segmentation of the LV cavity and myocardium, with potential to benefit the precision of LV function assessment.