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Nuclei segmentation and classification are two important tasks in the histopathology image analysis, because the mor- phological features of nuclei and spatial distributions of dif- ferent 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 net- works on a relatively small lung cancer dataset. Experimental results prove the effectiveness of the proposed method. The code is publicly available
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Publication Date:
Journal Name:
2019 IEEE 16th International Symposium on Biomedical Imaging (ISBI 2019)
Sponsoring Org:
National Science Foundation
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