MDC-net: A new convolutional neural network for nucleus segmentation in histopathology images with distance maps and contour information

Accurate segmentation of nuclei in digital pathology images can assist doctors in diagnosing diseases and evaluating subsequent treatments. Manual segmentation of nuclei from pathology images is time-consuming because of the large number of nuclei and is also error-prone. Therefore, accurate and automatic nucleus segmentation methods are required. Owing to the large variations in the characterization of nuclei, it is difficult to accurately segment nuclei using traditional methods. In this study, we propose a new method for nucleus segmentation. The proposed method uses a deep fully convolutional neural network to perform end-to-end segmentation on pathological tissue slices. Multiple short residual connections were used to fuse feature maps from different scales to better utilize the context information. Dilated convolutions with different dilation ratios were used to increase the receptive fields. In addition, we incorporated the distance map and contour information into the segmentation method to segment touching nuclei, which is difficult via traditional segmentation methods. Finally, post-processing was used to improve the segmentation results. The results demonstrate that our segmentation method can obtain comparable or better performance than other state-of-the-art methods on the public nuclei histopathology datasets.

Automated summary

This study introduces a new method for nucleus segmentation in pathological images using a deep neural network, which utilizes multiple short residual connections and dilated convolutions to improve accuracy. Additionally, distance map and contour information are incorporated to address the segmentation of touching nuclei.