Segmentation of the right ventricular myocardial infarction in multi-centre cardiac magnetic resonance images

Abstract

Right ventricular myocardial infarction (RVMI) is associated with higher in-hospital morbidity and mortality. Cardiac magnetic resonance (CMR) imaging provides crucial pathological information for diagnosis and/or treatment of RVMI. Segmentation of RVMI in CMR images is significant but challenging. This is because, to the best of our knowledge, there is no publicly available dataset in this field. Furthermore, the severe class imbalance problem caused by mostly less than 0.2 % proportion and the extreme intensity overlap between RVMI and the background bring challenges to the design of segmentation model. Therefore, we release a benchmark CMR dataset, consist of short-axis MR images of 213 subjects from 3 centres acquired by Philips, GE, and Siemens equipments. A multi-stage sequential deep learning model RVMISegNet is proposed to segment RVMI and its related organs at different scales to tackle the class imbalance and intensity overlap problems. In the first stage, transfer learning is employed to localize the right ventricle region. In the second stage, the centroid of the right ventricle guides the extraction of a region of interest, where pseudo-labels are generated to assist a coarse segmentation of myocardial infarction. In the third stage, morphological post-processing is applied, and fine segmentation is performed. Both the coarse and fine segmentation stages use a modified UNet++ backbone, which integrates texture and semantic extraction modules. Extensive experiments validate the state-of-the-art performance of our model and the effectiveness of its constituent modules. The dataset and source codes are available at https://github.com/DFLAG-NEU/RVMISegNet.