A review of heart chamber segmentation for structural and functional analysis using cardiac magnetic resonance imaging

Abstract

Cardiovascular magnetic resonance (CMR) has become a key imaging modality in clinical cardiology practice due to its unique capabilities for non-invasive imaging of the cardiac chambers and great vessels. A wide range of CMR sequences have been developed to assess various aspects of cardiac structure and function, and significant advances have also been made in terms of imaging quality and acquisition times. A lot of research has been dedicated to the development of global and regional quantitative CMR indices that help the distinction between health and pathology. The goal of this review paper is to discuss the structural and functional CMR indices that have been proposed thus far for clinical assessment of the cardiac chambers. We include indices definitions, the requirements for the calculations, exemplar applications in cardiovascular diseases, and the corresponding normal ranges. Furthermore, we review the most recent state-of-the art techniques for the automatic segmentation of the cardiac boundaries, which are necessary for the calculation of the CMR indices. Finally, we provide a detailed discussion of the existing literature and of the future challenges that need to be addressed to enable a more robust and comprehensive assessment of the cardiac chambers in clinical practice.

Heart chamber segmentation from CT using convolutional neural networks

“…Cardiologists and surgeons use CT images for planning procedures to correct septum defects and diagnosing congenital heart disease 6, 7 . Precise chamber-specific segmentation can also be used to assess cardiac function using metrics such as chamber volume, ejection fraction, and myocardial mass 8 .…”

A Deep Learning Network for Right Ventricle Segmentation in Short:Axis MRI

“…Additionally, the related review papers [18,19] summarize more methods in new views. In this paper, inspired by the paper [20], we proposed a new method to address the RV segmentation problems in CMR using deep CNN networks.…”

Unsupervised Myocardial Segmentation for Cardiac BOLD

“…The automated myocardial segmentation for standard CINE MR is a well-studied problem [11], [12]. Most of the algorithms used for CINE MRI can be broadly classified into two categories based on whether the methodology is unsupervised or supervised.…”