SeUNet-Trans: A Simple yet Effective UNet-Transformer Model for Medical Image Segmentation

Automated medical image segmentation is becoming increasingly crucial in modern clinical practice, driven by the growing demand for precise diagnoses, the push towards personalized treatment plans, and advancements in machine learning algorithms, especially the incorporation of deep learning methods. While convolutional neural networks (CNNs) have been prevalent among these methods, the remarkable potential of Transformer-based models for computer vision tasks is gaining more acknowledgment. To harness the advantages of both CNN-based and Transformer-based models, we propose a simple yet effective UNet-Transformer (seUNet-Trans) model for medical image segmentation. In our approach, the UNet model is designed as a feature extractor to generate multiple feature maps from the input images, and these maps are propagated into a bridge layer, which sequentially connects the UNet and the Transformer. In this stage, we employ the pixel-level embedding technique without position embedding vectors to make the model more efficient. Moreover, we applied spatial-reduction attention in the Transformer to reduce the computational/memory overhead. By leveraging the UNet architecture and the self-attention mechanism, our model not only preserves both local and global context information but also captures long-range dependencies between input elements. The proposed model is extensively experimented on five medical image segmentation datasets, including polyp segmentation, to demonstrate its efficacy. A comparison with several state-of-the-art segmentation models on these datasets shows the superior performance of seUNet-Trans.