Hybrid U-Net Model with Visual Transformers for Enhanced Multi-Organ Medical Image Segmentation

Medical image segmentation is an essential process that facilitates the precise extraction and localization of diseased areas from medical pictures. It can provide clear and quantifiable information to support clinicians in making final decisions. However, due to the lack of explicit modeling of global relationships in CNNs, they are unable to fully use the long-range dependencies among several image locations. In this paper, we propose a novel model that can extract local and global semantic features from the images by utilizing CNN and the visual transformer in the encoder. It is important to note that the self-attention mechanism treats a 2D image as a 1D sequence of patches, which can potentially disrupt the image’s inherent 2D spatial structure. Therefore, we utilized the structure of the transformer using visual attention and large kernel attention, and we added a residual convolutional attention module (RCAM) and multi-scale fusion convolution (MFC) into the decoder. They can help the model better capture crucial features and fine details to improve detail and accuracy of segmentation effects. On the synapse multi-organ segmentation (Synapse) and the automated cardiac diagnostic challenge (ACDC) datasets, our model performed better than the previous models, demonstrating that it is more precise and robust in multi-organ medical image segmentation.