A lightweight multi-modality medical image semantic segmentation network base on the novel UNeXt and Wave-MLP.

Medical images sometimes contain diseased regions that are different sizes and. shapes, which makes it difficult to accurately segment these areas or their edges. However, directly coupling CNN and MLP to construct global and local dependency. models may also cause significant computational complexity issues. In this paper, a. unique, lightweight UNeXt network segmentation model for medical images based on. dynamic aggregation tokens was proposed. Firstly, the Wave Block module in Wave-MLP was introduced to replace the Tok-MLP module in UNeXt. The phase term in Wave Block can dynamically aggregate tokens, improving the segmentation accuracy of the model. Secondly, an AG attention gate module is added at the skip connection to suppress irrelevant feature representations in the sampling path of the encoding. network, thereby reducing computational costs and paying attention to noise and artifacts. Finally, the Focal Tversky Loss was added to handle both binary and multiple classification jobs. Quantitative and qualitative experiments were conducted on two public datasets: COVID-19 CT and BraTS 2018 MRI. The Dice score, Precision score, recall score, and Iou score of the proposed model on the COVID-19 dataset were 0.928, 0.867, 0.916, and 0.940, respectively. On BraTS 2018, the Dice scores of the ET, WT, and TC categories were 0.933, 0.925, and 0.918, respectively, and the HD scores were 1.595, 2.348, and 1.549, respectively. At the same time, the model is lightweight and has a considerably decreased training time with GFLOPs and Params of 0.52 and 0.76, respectively. The proposed lightweight model is superior to other existing methods in terms of segmentation accuracy and computing complexity according to experimental data.