TransCUNet: UNet cross fused transformer for medical image segmentation

Accurate segmentation of medical images is crucial for clinical diagnosis and evaluation. However, medical images have complex shapes, the structures of different objects are very different, and most medical datasets are small in scale, making it difficult to train effectively. These problems increase the difficulty of automatic segmentation. To further improve the segmentation performance of the model, we propose a multi-branch network model, called TransCUNet, for segmenting medical images of different modalities. The model contains three structures: cross residual fusion block (CRFB), pyramidal pooling module (PPM) and gated axial-attention, which achieve effective extraction of high-level and low-level features of images, while showing high robustness to different size segmentation objects and different scale datasets. In our experiments, we use four datasets to train, validate and test the models. The experimental results show that TransCUNet has better segmentation performance compared to the current mainstream segmentation methods, and the model has a smaller size and number of parameters, which has great potential for clinical applications. •Multi-branch network model, cross residual fusion block, global features.•Pyramidal pooling module, contextual information.•Gated axial-attention, gated axial transformer, high-level and low-level features.•Cross residual fusion block, faster converge.