DCNet: Diversity convolutional network for ventricle segmentation on short-axis cardiac magnetic resonance images

To accurately and simultaneously segment myocardium, left and right ventricles at the end-diastolic (ED) and end-systolic (ES) phases from short-axis cardiac magnetic resonance (CMR) images with inherent variability in appearance, shape, and location of the region of interest (ROI), we propose a diversity convolutional network (DCNet) that aims to solve ventricle under- and over-segmentation problems. DCNet is composed of three stages: the integration of diversity features, recoding of diversity features, and decoding of integrated features. To enhance the representational capacity and enrich the feature space of a single convolution, we design a diversity convolution block during the first stage. During the second stage, we design a dual-path channel attention mechanism to simultaneously select average and maximum features. In addition, we use a soft Dice loss function to assist in the network’s training. We conducted experiments on the 2017 Automated Cardiac Diagnosis Challenge (ACDC 2017), 2019 Multi-Sequence Cardiac MR Segmentation Challenge (MS-CMRSeg 2019), and 2020 Myocardial Pathology Segmentation Challenge (MyoPS 2020) datasets. We submitted our test results on the ACDC dataset to an online test platform, the proposed DCNet achieved Dice scores of 95.80%, 91.77%, and 91.57% in the left ventricle, right ventricle, and myocardium segmentation tasks, respectively. Compared with four representative networks, the proposed DCNet achieves the best results on balanced steady state free precession (bSSFP) cine sequence and late gadolinium enhancement (LGE) CMR sequences on the MS-CMRSeg and MyoPS datasets. Therefore, the proposed method is promising for automatic ventricle segmentation in clinical applications. We uploaded the code to .
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