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 FullWidth-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