Deep Learning Based Multi-modal Cardiac MR Image Segmentation

Abstract

Accurate modelling and segmentation of the ventricles and myocardium in cardiac MR (CMR) image is crucial for diagnosis and treatment management for patients suffering from myocardial infarction (MI). As the infarcted myocardium can be enhanced in LGE CMR through appearing with distinctive brightness compared with the healthy tissues, it can help doctors better study the presence, location, and extent of MI in clinical diagnosis. Hence it is of great significance to delineate ventricles and myocardium from LGE CMR images. In this study, we proposed a multi-modal cardiac MR image segmentation strategy via combining the T2-weighted CMR and the balanced-Steady State Free Precession (bSSFP) CMR sequence. Specifically, the T2-weighted CMR and bSSFP are co-registered and set as the input of the convolution neural network to do the first stage segmentation in bSSFP space. By predicting all the labels, we further registered T2-weighted CMR, bSSFP and the corresponding labels into LGE space, and as an input to the convolution neural network to do the second stage segmentation. In the end, we post-processed the output masks to further ensure the accuracy of the segmentation results. The dice score of the proposed method in test set of Multi-sequence Cardiac MR (MS-CMR) Challenge 2019 achievers 0.8541, 0.7131 and 0.7924 for left ventricular (LV), left ventricular myocardium (LV myo), and right ventricular (RV).