MufiNet: Multiscale Fusion Residual Networks for Medical Image Segmentation

U-Net has been considered as an outstanding deep learning neural network in medical image segmentation problems. The segmentation results of the U-Net based model, however, are always too conservative and smooth. MufiNet, a segmentation model using multiple U-Net chains (with multiple encoder-decoder branches), is proposed in this paper. It can fuse the receptive fields obtained from different scales. The convolution layer of 1 x 1 is introduced to add the residual connection to enhance the adaptability to the depth of the network. The multi-scale fusion module with residuals is combined with the U-Net chain architecture to retain more information flow paths, and the multi-scale context information is used to improve the performance and robustness of the segmented network. MufiNet model is extensively evaluated on three datasets in this paper, including two benchmark datasets (lung segmentation and skin cancer lesion segmentation) and cervical cancer dataset jointly constructed with a hospital. The experimental results show that MufiNet could yield better performance in medical image segmentation tasks than U-Net and LadderNet models.