Cardiac segmentation on late gadolinium enhancement MRI: A benchmark study from multi-sequence cardiac MR segmentation challenge

Accurate computing, analysis and modeling of the ventricles and myocardium from medical images are important, especially in the diagnosis and treatment management for patients suffering from myocardial infarction (MI). Late gadolinium enhancement (LGE) cardiac magnetic resonance (CMR) provides an im-portant protocol to visualize MI. However, compared with the other sequences LGE CMR images with gold standard labels are particularly limited. This paper presents the selective results from the Multi-Sequence Cardiac MR (MS-CMR) Segmentation challenge, in conjunction with MICCAI 2019. The challenge offered a data set of paired MS-CMR images, including auxiliary CMR sequences as well as LGE CMR, from 45 patients who underwent cardiomyopathy. It was aimed to develop new algorithms, as well as benchmark existing ones for LGE CMR segmentation focusing on myocardial wall of the left ventricle and blood cav-ity of the two ventricles. In addition, the paired MS-CMR images could enable algorithms to combine the complementary information from the other sequences for the ventricle segmentation of LGE CMR. Nine representative works were selected for evaluation and comparisons, among which three methods are un-supervised domain adaptation (UDA) methods and the other six are supervised. The results showed that the average performance of the nine methods was comparable to the inter-observer variations. Particu-larly, the top-ranking algorithms from both the supervised and UDA methods could generate reliable and robust segmentation results. The success of these methods was mainly attributed to the inclusion of the auxiliary sequences from the MS-CMR images, which provide important label information for the training of deep neural networks. The challenge continues as an ongoing resource, and the gold standard segmen-tation as well as the MS-CMR images of both the training and test data are available upon registration via its homepage ( www.sdspeople.fudan.edu.cn/zhuangxiahai/0/mscmrseg/ ).(c) 2022 Elsevier B.V. All rights reserved.

Automated summary

Accurate computing, analysis, and modeling of ventricles and myocardium are crucial for the diagnosis and treatment management of patients with myocardial infarction. This paper presents the selective results from a Multi-Sequence Cardiac MR Segmentation challenge, which provided a dataset of paired MS-CMR images for algorithm development and benchmarking. The results showed that the top-ranking algorithms could generate reliable and robust segmentation results, thanks to the inclusion of auxiliary sequences from MS-CMR images.