Automatic deep learning-based myocardial infarction segmentation from delayed enhancement MRI. (January 2022)

• Record Type: Journal Article
• Authors: Chen, Zhihao; Lalande, Alain; Salomon, Michel; Decourselle, Thomas; Pommier, Thibaut; Qayyum, Abdul; Shi, Jixi; Perrot, Gilles; Couturier, Raphaël
• Is Part Of: Computerized medical imaging and graphics. Volume 95(2022)
• Publication Date: 2022-01
• Subjects: CNN -- Semantic segmentation -- DE-MRI -- Myocardial infarction -- Adaptive framework; Diagnostic imaging -- Periodicals; Imaging systems in medicine -- Periodicals; Diagnosis, Radioscopic -- Data processing -- Periodicals; Diagnostic Imaging -- Periodicals; Imagerie pour le diagnostic -- Périodiques; Diagnostic imaging; Periodicals; Electronic journals; Electronic journals; 616.0754
• DOI: 10.1016/j.compmedimag.2021.102014
• Languages: English
• ISSNs: 0895-6111

Abstract

Abstract: Delayed Enhancement cardiac MRI (DE-MRI) has become indispensable for the diagnosis of myocardial diseases. However, to quantify the disease severity, doctors need time to manually annotate the scar and myocardium. To address this issue, in this paper we propose an automatic myocardial infarction segmentation approach on the left ventricle from short-axis DE-MRI based on Convolutional Neural Networks (CNN). The objective is to segment myocardial infarction on short-axis DE-MRI images of the left ventricle acquired 10 min after the injection of a gadolinium-based contrast agent. The segmentation of the infarction area is realized in two stages: a first CNN model finds the contour of myocardium and a second CNN model segments the infarction. Compared to the manual intra-observer and inter-observer variations for the segmentation of myocardial infarction, and to the automatic segmentation with Gaussian Mixture Model, our proposal achieves satisfying segmentation results on our dataset of 904 DE-MRI slices.