MTC-Net: Multi-scale feature fusion network for medical image segmentation

Image segmentation is critical in medical image processing for lesion detection, localisation, and subsequent diagnosis. Currently, computer-aided diagnosis (CAD) has played a significant role in improving diagnostic efficiency and accuracy. The segmentation task is made more difficult by the hazy lesion boundaries and uneven forms. Because standard convolutional neural networks (CNNs) are incapable of capturing global contextual information, adequate segmentation results are impossible to achieve. We propose a multiscale feature fusion network (MTC-Net) in this paper that integrates deep separable convolution and self-attentive modules in the encoder to achieve better local continuity of images and feature maps. In the decoder, a multi-branch multi-scale feature fusion module (MSFB) is utilized to improve the network’s feature extraction capability, and it is integrated with a global cooperative aggregation module (GCAM) to learn more contextual information and adaptively fuse multi-scale features. To develop rich hierarchical representations of irregular forms, the suggested detail enhancement module (DEM) adaptively integrates local characteristics with their global dependencies. To validate the effectiveness of the proposed network, we conducted extensive experiments, evaluated on the public datasets of skin, breast, thyroid and gastrointestinal tract with ISIC2018, BUSI, TN3K and Kvasir-SEG. The comparison with the latest methods also verifies the superiority of our proposed MTC-Net in terms of accuracy. Our code on https://github.com/gih23/MTC-Net.