title: 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.
authors.affiliation: Department of Electronic and Electrical Engineering, The University of Sheffield, Sheffield, S1 3JD, UK.
authors.forename: Peng
authors.initials: P
authors.lastname: Peng
authors.affiliation: Universitat Pompeu Fabra, 08002, Barcelona, Spain.
authors.forename: Karim
authors.initials: K
authors.lastname: Lekadir
authors.affiliation: Department of Electronic and Electrical Engineering, The University of Sheffield, Sheffield, S1 3JD, UK.
authors.forename: Ali
authors.initials: A
authors.lastname: Gooya
authors.affiliation: Department of Computer Science and Digital Technologies, Northumbria University, Newcastle upon Tyne, NE1 8ST, UK.
authors.forename: Ling
authors.initials: L
authors.lastname: Shao
authors.affiliation: Centre Lead for Advanced Cardiovascular Imaging, William Harvey Research Institute, Queen Mary University of London, London, EC1M 6BQ, UK.
authors.forename: Steffen E
authors.initials: SE
authors.lastname: Petersen
authors.affiliation: Department of Electronic and Electrical Engineering, The University of Sheffield, Sheffield, S1 3JD, UK. a.frangi@sheffield.ac.uk.
authors.forename: Alejandro F
authors.identifier: http://orcid.org/0000-0002-2675-528X
authors.initials: AF
authors.lastname: Frangi
mesh_terms: D006321:Heart / Q000000981:diagnostic imaging*; D006331:Heart Diseases / Q000000981:diagnostic imaging* / Q000473:pathology*; D006334:Heart Function Tests / Q000379:methods*; D006801:Humans; D007089:Image Enhancement / Q000379:methods; D007090:Image Interpretation, Computer-Assisted / Q000379:methods; D021621:Imaging, Three-Dimensional / Q000379:methods; D000069550:Machine Learning; D019028:Magnetic Resonance Imaging, Cine / Q000379:methods*; D009206:Myocardium / Q000473:pathology*; D010363:Pattern Recognition, Automated / Q000379:methods; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity
keywords: Cardiac segmentation; Clinical assessment; MRI
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