Joint Segmentation and Fine-Grained Classification of Nuclei in Histopathology Images
Hui Yan Qu, Gregory Riedlinger, Pengxiang Wu, Qiaoying Huang, Jingru Yi, Subhajyoti De, Dimitris Metaxas · 2019
Nuclei segmentation and classification are two important tasks in the histopathology image analysis, because the morphological features of nuclei and spatial distributions of different types of nuclei are highly related to cancer diagnosis and prognosis. Existing methods handle the two problems independently, which are not able to obtain the features and spatial heterogeneity of different types of nuclei at the same time. In this paper, we propose a novel deep learning based method which solves both tasks in a unified framework. It can segment individual nuclei and classify them into tumor, lymphocyte and stroma nuclei. Perceptual loss is utilized to enhance the segmentation of details. We also take advantages of transfer learning to promote the training of deep neural networks on a relatively small lung cancer dataset. Experimental results prove the effectiveness of the proposed method. The code is publicly available.