Multimodal parallel attention network for medical image segmentation

• Published in: Image and vision computing Vol. 147; p. 105069
• Main Authors: Wang, Zhibing, Wang, Wenmin, Li, Nannan, Zhang, Shenyong, Chen, Qi, Jiang, Zhe
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 01.07.2024
• Subjects: Channel parallel; Feature parallel; Medical image segmentation; Multimodal parallel attention; Spatial parallel; Spatial parallel; Multimodal parallel attention; Feature parallel; Channel parallel; Medical image segmentation
• ISSN: 0262-8856; 1872-8138
• DOI: 10.1016/j.imavis.2024.105069

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. •Architecture integrates multimodal parallel attention for segmentation.•Network employs feature, spatial, and channel parallel attention.•Radiologists aided in precise lesion localization across organs.•Identification rate of lesions improved, aiding disease diagnosis.