Ensemble of 2D Residual Neural Networks Integrated with Atrous Spatial Pyramid Pooling Module for Myocardium Segmentation of Left Ventricle Cardiac MRI

Iftikhar Ahmad, Abdul Qayyum, Brij B. Gupta, Madini O. Alassafi and Rayed A. AlGhamdi
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Iftikhar Ahmad: Department of Information Technology, Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah 21589, Saudi Arabia
Abdul Qayyum: ImViA Lab, Department of Information System, University of Burgundy, 21000 Dijon, France
Brij B. Gupta: Department of Computer Engineering, National Institute of Technology Kurukshetra, Kurukshetra 136119, Haryana, India
Madini O. Alassafi: Department of Information Technology, Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah 21589, Saudi Arabia
Rayed A. AlGhamdi: Department of Information Technology, Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah 21589, Saudi Arabia

Mathematics, 2022, vol. 10, issue 4, 1-23

Abstract: Cardiac disease diagnosis and identification is problematic mostly by inaccurate segmentation of the cardiac left ventricle (LV). Besides, LV segmentation is challenging since it involves complex and variable cardiac structures in terms of components and the intricacy of time-based crescendos. In addition, full segmentation and quantification of the LV myocardium border is even more challenging because of different shapes and sizes of the myocardium border zone. The foremost purpose of this research is to design a precise automatic segmentation technique employing deep learning models for the myocardium border using cardiac magnetic resonance imaging (MRI). The ASPP module (Atrous Spatial Pyramid Pooling) was integrated with a proposed 2D-residual neural network for segmentation of the myocardium border using a cardiac MRI dataset. Further, the ensemble technique based on a majority voting ensemble method was used to blend the results of recent deep learning models on different set of hyperparameters. The proposed model produced an 85.43% dice score on validation samples and 98.23% on training samples and provided excellent performance compared to recent deep learning models. The myocardium border was successfully segmented across diverse subject slices with different shapes, sizes and contrast using the proposed deep learning ensemble models. The proposed model can be employed for automatic detection and segmentation of the myocardium border for precise quantification of reflow, myocardial infarction, myocarditis, and h cardiomyopathy (HCM) for clinical applications.

Keywords: myocardium segmentation; cardiac MRI; deep learning segmentation models; ASPP; residual neural network (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2022
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