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

Abstract

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.