CTW-Net: A Deeper Multiscale Feature Fusion W-Shaped Network for Medical Image Segmentation

Nowadays, convolutional neural networks (CNNs) are widely adopted in medical image analysis. Nevertheless, the inherent local nature of the convolution operator leads to limitations in capturing long remote dependencies. Fortunately, the self-attentive mechanism in Transformers can effectively model long remote dependencies. Therefore, we construct a hybrid network (CTW-Net) for medical image segmentation that combines the benefits of CNN and Transformer more effectively. We first construct a pure convolutional multiscale transformer (PCM Transformer) to multiscale the features obtained from the hierarchical encoder and decoder. It utilizes Hadamard product for second-order spatial interactions to fuse features at multiple scales, enabling the network to concentrate on more significant regions of lesion characteristics. Besides, we developed an efficient maximum pooling residual block (EMPR) based on convolution and maximum pooling. The EMPR block enables the complementation of locally important features in the downsampling process, which raises the performance of lesion segmentation. Experimental outcomes reveal that our network has achieved excellent segmentation performance in breast ultrasound and colon polyp lesion segmentation.