MultiIB‐TransUNet: Transformer with multiple information bottleneck blocks for CT and ultrasound image segmentation

Abstract Background Accurate medical image segmentation is crucial for disease diagnosis and surgical planning. Transformer networks offer a promising alternative for medical image segmentation as they can learn global features through self‐attention mechanisms. To further enhance performance, many researchers have incorporated more Transformer layers into their models. However, this approach often results in the model parameters increasing significantly, causing a potential rise in complexity. Moreover, the datasets of medical image segmentation usually have fewer samples, which leads to the risk of overfitting of the model. Purpose This paper aims to design a medical image segmentation model that has fewer parameters and can effectively alleviate overfitting. Methods We design a MultiIB‐Transformer structure consisting of a single Transformer layer and multiple information bottleneck (IB) blocks. The Transformer layer is used to capture long‐distance spatial relationships to extract global feature information. The IB block is used to compress noise and improve model robustness. The advantage of this structure is that it only needs one Transformer layer to achieve the state‐of‐the‐art (SOTA) performance, significantly reducing the number of model parameters. In addition, we designed a new skip connection structure. It only needs two 1× 1 convolutions, the high‐resolution feature map can effectively have both semantic and spatial information, thereby alleviating the semantic gap. Results The proposed model is on the Breast UltraSound Images (BUSI) dataset, and the IoU and F1 evaluation indicators are 67.75 and 87.78. On the Synapse multi‐organ segmentation dataset, the Param, Hausdorff Distance (HD) and Dice Similarity Cofficient (DSC) evaluation indicators are 22.30, 20.04 and 81.83. Conclusions Our proposed model (MultiIB‐TransUNet) achieved superior results with fewer parameters compared to other models.