  Recently, deep learning has become much more popular in computer vision area.
The Convolution Neural Network (CNN) has brought a breakthrough in images
segmentation areas, especially, for medical images. In this regard, U-Net is
the predominant approach to medical image segmentation task. The U-Net not only
performs well in segmenting multimodal medical images generally, but also in
some tough cases of them. However, we found that the classical U-Net
architecture has limitation in several aspects. 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 build the DC-UNet based on this CNN. We have evaluated our
model on three datasets with tough cases and have obtained a relative
improvement in performance of 2.90%, 1.49% and 11.42% respectively compared
with classical U-Net. In addition, we used the Tanimoto similarity to replace
the Jaccard similarity for gray-to-gray image comparisons.
