MTC-TransUNet: A Multi-Scale Mixed Convolution TransUNet for Medical Image Segmentation

Medical image segmentation is an important means to assist doctors in making accurate diagnoses. However, medical images are often subject to noise interference, and the high similarity between the background and target areas also increases the difficulty of segmentation, hence there is room for improvement in segmentation accuracy. To address these challenges, this paper proposes a multi-scale mixed convolutional image segmentation network. The network consists of two main parts: an encoder and a decoder. The encoder is formed by a combination of mixed convolutional module and multi-scale Transformer module, and the mixed convolutional module uses a blend of 2D and 3D convolutions to enhance feature extraction capabilities. Additionally, we designed a multi-scale Transformer module aimed at capturing long-range correlation information at multiple scales. In the decoding phase, we introduce coordinate convolution for the first time in the U-Net architecture to replace traditional convolution, thereby enhancing the model's spatial localization capabilities. In experimental validations on multiple public datasets, MTC-TransUNet outperforms other networks.