HCA-former: Hybrid Convolution Attention Transformer for 3D Medical Image Segmentation

Abstract

In recent years, Transformers have achieved success in the field of medical image segmentation due to their outstanding capability to model long-range dependencies. However, many existing segmentation methods only use Transformer as an auxiliary module to capture global context information in images, limiting the potential of the Transformers. Additionally, self-attention mechanism within the Transformers can lead to attention collapse issues, thus triggering semantic gap between the encoder and decoder. Furthermore, most networks have difficulties in effectively handling multi-scale and multi-channel feature information. To address the above problems, we propose a hybrid Convolutional Neural Networks (CNNs) and Transformers method for medical image segmentation (HCA-Former). We design a local multi-channel attention block (LMCA) to effectively combine the features of CNN and Transformers, enabling multi-channel information extraction and interaction. Using the Double-Former Block (DFB) alleviates the semantic gap between the encoder and decoder, restoring more detailed information. Moreover, the utilization of the global multi-scale attention block (GMSA) can establish information interaction among multi-scale targets, thereby enhancing generalization capability of the model. To validate the effectiveness of our approach, we evaluate the proposed method on three challenging tasks: the MICCAI 2015 Multi-Image Abdominal Marker Challenge (Synapse), Automated Cardiac Diagnosis Dataset (ACDC), and Medical Segmentation Decathlon Brain Tumor Segmentation (MSD brain tumor). Extensive experiments demonstrate that our HCA-Former achieved competitive or better performance than state-of-the-art approaches for 3D medical image segmentation.