MDC-net: A new convolutional neural network for nucleus segmentation in histopathology images with distance maps and contour information
Department of Applied Computing
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.
Computers in Biology and Medicine
MDC-net: A new convolutional neural network for nucleus segmentation in histopathology images with distance maps and contour information.
Computers in Biology and Medicine,
Retrieved from: https://digitalcommons.mtu.edu/michigantech-p/14991