- Session 1: Deep Learning -- Day 2 (Nov.18), talks: 09:00-11:00 (5th floor Hall 1), poster session: 11:00-13:30
- Poster number: Mon15
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Xiaoming Liu (Wuhan University of Science and Technology); Zhengsheng Guo (Wuhan University of Science and Technology); Bo Li (Wuhan University of Science and Technology); Jun Cao (Wuhan University of Science and Technology)
Accurate access to nuclear information on digital pathology images can assist physicians in diagnosis and subsequent treatment. The pathological images have a large number of nuclei and part of nuclei is touching, manual segmentation is time consuming and error prone. Therefore it is an important task to develop a accurate nuclei segmentation method. For traditional methods, it is hard to obtain a accurately nuclei segmentation result, because the nuclei have many different characterizations. In this paper, we propose a new nuclei segmentation method (MDC-Net), which is a deep fully convolutional network. The network contains multiple residual operations to reduce detail loss in image. In addition, dilated convolution which has different dilation ratio is used to increase receptive field. MDC-Net contains the distance map and contour image, enhancing information on individual nuclei to get accurate segmentation results. We improve the segmentation effect by using the post-processing operate. We demonstrate that MDC-Net can obtain state-of-the-art results on public dataset with multiple organ slices compared with other popular methods.