title: Evaluation of state-of-the-art segmentation algorithms for left ventricle infarct from late Gadolinium enhancement MR images.
abstract: Studies have demonstrated the feasibility of late Gadolinium enhancement (LGE) cardiovascular magnetic resonance (CMR) imaging for guiding the management of patients with sequelae to myocardial infarction, such as ventricular tachycardia and heart failure. Clinical implementation of these developments necessitates a reproducible and reliable segmentation of the infarcted regions. It is challenging to compare new algorithms for infarct segmentation in the left ventricle (LV) with existing algorithms. Benchmarking datasets with evaluation strategies are much needed to facilitate comparison. This manuscript presents a benchmarking evaluation framework for future algorithms that segment infarct from LGE CMR of the LV. The image database consists of 30 LGE CMR images of both humans and pigs that were acquired from two separate imaging centres. A consensus ground truth was obtained for all data using maximum likelihood estimation. Six widely-used fixed-thresholding methods and five recently developed algorithms are tested on the benchmarking framework. Results demonstrate that the algorithms have better overlap with the consensus ground truth than most of the n-SD fixed-thresholding methods, with the exception of the Full-Width-at-Half-Maximum (FWHM) fixed-thresholding method. Some of the pitfalls of fixed thresholding methods are demonstrated in this work. The benchmarking evaluation framework, which is a contribution of this work, can be used to test and benchmark future algorithms that detect and quantify infarct in LGE CMR images of the LV. The datasets, ground truth and evaluation code have been made publicly available through the website: https://www.cardiacatlas.org/web/guest/challenges.
authors.affiliation: Department of Imaging Sciences & Biomedical Engineering, King's College London, UK. Electronic address: rashed.karim@kcl.ac.uk.
authors.forename: Rashed
authors.initials: R
authors.lastname: Karim
authors.affiliation: Department of Cardiology, Haga Teaching Hospital, The Netherlands.
authors.forename: Pranav
authors.initials: P
authors.lastname: Bhagirath
authors.affiliation: Cardiovascular Imaging and Dynamics, Department of Cardiovascular Sciences, Universiteit Leuven, Belgium.
authors.forename: Piet
authors.initials: P
authors.lastname: Claus
authors.affiliation: Cardiovascular Imaging and Dynamics, Department of Cardiovascular Sciences, Universiteit Leuven, Belgium.
authors.forename: R
authors.initials: R
authors.lastname: James Housden
authors.affiliation: Department of Imaging Sciences & Biomedical Engineering, King's College London, UK.
authors.forename: Zhong
authors.initials: Z
authors.lastname: Chen
authors.affiliation: The Centre for Intelligence Machines, McGill University, Canada.
authors.forename: Zahra
authors.initials: Z
authors.lastname: Karimaghaloo
authors.affiliation: Department of Imaging Sciences & Biomedical Engineering, King's College London, UK.
authors.forename: Hyon-Mok
authors.initials: HM
authors.lastname: Sohn
authors.affiliation: Alma IT Systems, Spain.
authors.forename: Laura
authors.initials: L
authors.lastname: Lara Rodríguez
authors.affiliation: Alma IT Systems, Spain.
authors.forename: Sergio
authors.initials: S
authors.lastname: Vera
authors.affiliation: Centre for Computational Imaging and Simulation Technologies in Biomedicine (CISTIB), Department of Information and Communication Technologies, Universitat Pompeu Fabra, Barcelona, Spain.
authors.forename: Xènia
authors.initials: X
authors.lastname: Albà
authors.affiliation: Fraunhofer Institute for Medical Image Computing, Fraunhofer MEVIS, Germany.
authors.forename: Anja
authors.initials: A
authors.lastname: Hennemuth
authors.affiliation: Fraunhofer Institute for Medical Image Computing, Fraunhofer MEVIS, Germany.
authors.forename: Heinz-Otto
authors.initials: HO
authors.lastname: Peitgen
authors.affiliation: The Centre for Intelligence Machines, McGill University, Canada.
authors.forename: Tal
authors.initials: T
authors.lastname: Arbel
authors.affiliation: Alma IT Systems, Spain; ICREA, Spain; SIMBIOsys Research Group, Universitat Pompeu Fabra, Spain.
authors.forename: Miguel A
authors.initials: MA
authors.lastname: Gonzàlez Ballester
authors.affiliation: Centre for Computational Imaging and Simulation Technologies in Biomedicine (CISTIB), Department of Electronic & Electrical Engineering, University of Sheffield, Sheffield, UK.
authors.forename: Alejandro F
authors.initials: AF
authors.lastname: Frangi
authors.affiliation: Department of Cardiology, Haga Teaching Hospital, The Netherlands.
authors.forename: Marco
authors.initials: M
authors.lastname: Götte
authors.affiliation: Department of Imaging Sciences & Biomedical Engineering, King's College London, UK.
authors.forename: Reza
authors.initials: R
authors.lastname: Razavi
authors.affiliation: Department of Imaging Sciences & Biomedical Engineering, King's College London, UK.
authors.forename: Tobias
authors.initials: T
authors.lastname: Schaeffter
authors.affiliation: Department of Imaging Sciences & Biomedical Engineering, King's College London, UK.
authors.forename: Kawal
authors.initials: K
authors.lastname: Rhode
mesh_terms: D000465:Algorithms; D000818:Animals; D003287:Contrast Media / Q000008:administration & dosage; D005682:Gadolinium / Q000008:administration & dosage*; D006801:Humans; D007089:Image Enhancement / Q000379:methods / Q000592:standards; D007090:Image Interpretation, Computer-Assisted / Q000379:methods / Q000592:standards; D008279:Magnetic Resonance Imaging / Q000592:standards*; D009203:Myocardial Infarction / Q000150:complications / Q000000981:diagnostic imaging*; D010363:Pattern Recognition, Automated / Q000592:standards*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D013552:Swine; D018487:Ventricular Dysfunction, Left / Q000000981:diagnostic imaging* / Q000209:etiology
chemical_list: D003287:Contrast Media; D005682:Gadolinium
keywords: Algorithm benchmarking; Late Gadolinium enhancement; Segmentation