Elsevier

Medical Image Analysis

Volume 30, May 2016, Pages 95-107
Medical Image Analysis

Evaluation of state-of-the-art segmentation algorithms for left ventricle infarct from late Gadolinium enhancement MR images

https://doi.org/10.1016/j.media.2016.01.004Get rights and content
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open access

Highlights

  • We present a standardisation for evaluating segmentation algorithms for late enhancement imaging of infarct in the left ventricle.

  • We demonstrate that widely used fixed thresholding methods such as the standard deviation method can have inaccuracies and is user-dependent.

  • We provide a consensus ground truth obtained with statistical modelling on the benchmark datasets. Future algorithms can thus be benchmarked to provide a more reliable result.

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.

Keywords

Late Gadolinium enhancement
Segmentation
Algorithm benchmarking

Cited by (0)

This paper was recommended for publication by James Duncan.