DC-UNet: rethinking the U-Net architecture with dual channel efficient CNN for medical image segmentation

Recently, deep learning has become much more popular in computer vision applications. The Convolutional Neural Network (CNN) has brought a breakthrough in image segmentation, especially for medical images. In this regard, the UNet is the predominant approach to the medical image segmentation task. The U-Net not only performs well in segmenting multimodal medical images generally, but also in some difficult cases. We found, however, that the classical U-Net architecture has limitations in several respects. Therefore, we applied modifications: 1) designed efficient CNN architecture to replace encoder and decoder, 2) applied residual module to replace skip connection between encoder and decoder to improve, based on the-state-of-the-art U-Net model. Following these modifications, we designed a novel architecture -- DC-UNet, as a potential successor to the U-Net architecture. We created a new effective CNN architecture and built the DC-UNet based on this CNN. We have evaluated our model on three datasets with difficult cases and have obtained a relative improvement in performance of 2.90%, 1.49%, and 11.42% respectively compared with classical UNet. In addition, we used the Tanimoto similarity measure to replace the Jaccard measure for gray-to-gray image comparisons.