A Symmetric Multiscale Detail-Guided Attention Network for Cardiac MR Image Semantic Segmentation

Cardiac medical image segmentation can advance healthcare and embedded vision systems. In this paper, a symmetric semantic segmentation architecture for cardiac magnetic resonance (MR) images based on a symmetric multiscale detail-guided attention network is presented. Detailed information and multiscale attention maps can be exploited more efficiently in this model. A symmetric encoder and decoder are used to generate high-dimensional semantic feature maps and segmentation masks, respectively. First, a series of densely connected residual blocks is introduced for extracting high-dimensional semantic features. Second, an asymmetric detail-guided module is proposed. In this module, a feature pyramid is used to extract detailed information and generate detailed feature maps as part of the detail guidance of the model during the training phase, which are used to extract deep features of multiscale information and calculate a detail loss with specific encoder semantic features. Third, a series of multiscale upsampling attention blocks symmetrical to the encoder is introduced in the decoder of the model. For each upsampling attention block, feature fusion is first performed on the previous-level low-resolution features and the symmetric skip connections of the same layer, and then spatial and channel attention are used to enhance the features. Image gradients of the input images are also introduced at the end of the decoder. Finally, the predicted segmentation masks are obtained by calculating a detail loss and a segmentation loss. Our method demonstrates outstanding performance on the public cardiac MR image dataset, which can achieve significant results for endocardial and epicardial segmentation of the left ventricle (LV).