Multimodal parallel attention network for medical image segmentation

Abstract

Medical image segmentation is a crucial aspect of medical image processing, and has been widely used in the detection and clinical diagnosis for brain, lung, liver, heart and other diseases. In this paper, we propose a novel multimodal parallel attention network, called MPA-Net, for medical image segmentation. MPA-Net is divided into two parts. The first part extracts more high-dimensional features by improved network structure, which contains the skip connection, the output of the multimodal parallel attention and the output of the previous upsampling layer. The second part incorporates a multimodal parallel attention mechanism, encompassing feature parallel attention, spatial parallel attention and channel parallel attention. This mechanism facilitates the effective fusion of high-dimensional and low-dimensional features, leading to enhanced context information. Experimental results on Kagglelung dataset, Liver dataset, Cell dataset, Drive dataset and Kvasir-SEG dataset show that MPA-Net has achieved better segmentation performance than that of other baseline methods, on lung, liver, cell contour, retinal vessel and polyps.