MDFA-Net: Multiscale dual-path feature aggregation network for cardiac segmentation on multi-sequence cardiac MR

Abstract

Deep convolutional neural networks have shown great potential in medical image segmentation. However, automatic cardiac segmentation is still challenging due to the heterogeneous intensity distributions and indistinct boundaries in the images. In this paper, we propose a multiscale dual-path feature aggregation network (MDFA-Net) to solve misclassification and shape discontinuity problems. The proposed network is aimed to maintain a realistic shape of the segmentation results and divided into two parts: the first part is a non-downsampling multiscale nested network (MN-Net) which restrains the cardiac continuous shape and maintains the shallow information, and the second part is a non-symmetric encoding and decoding network (nSED-Net) that can retain deep details and overcome misclassification. We conducted four-fold cross-validation experiments on balanced steady-state, free precession cine cardiac magnetic resonance (bSSFP cine CMR) sequence, edema-sensitive T2-weighted, black blood spectral presaturation attenuated inversion-recovery (T2-SPAIR) CMR sequence and late gadolinium enhancement (LGE) CMR sequence which include 45 cases in each sequence. The data are provided by the organizer of the Multi-sequence Cardiac MR Segmentation Challenge (MS-CMRSeg 2019) in conjunction with 2019 Medical Image Computing and Computer Assisted Interventions (MICCAI). We also conducted external validation experiments on the data of 2020 MICCAI myocardial pathology segmentation challenge (MyoPS 2020). Whether it is a four-fold cross-validation experiment or an external validation experiment, the proposed method ranks first or second in the segmentation tasks of multi-sequence CMR images. The subjective evaluation also shows the same results as the objective evaluation metrics. The code will be posted at https://github.com/fly1995/MDFA-Net/.