Nuclei instance segmentation with dual contour-enhanced adversarial network

Donghao Zhang, Yang Song, Siqi Liu, Dagan Feng, Yue Wang, Weidong Cai

Research output: Chapter in Book/Report/Conference proceedingConference PaperResearchpeer-review

18 Citations (Scopus)


The morphology of cancer cells is widely used by pathologists to grade stages of cancers. Accurate cancer cell segmentation is significant to obtain quantitative diagnosis. We proposed a dual contour-enhanced adversarial network to solve this challenge. The distance-transformed and contour-highlighted masks, and adversarial network are incorporated to improve individual cell segmentation capability. By evaluating quantitative individual cell segmentation results on 2017 MICCAI Digital Pathology Challenge, our method achieved best balance between precision and recall rate of individual cell segmentation compared to state-of-the-art cell segmentation methods.

Original languageEnglish
Title of host publicationProceedings of 2018 IEEE 15th International Symposium on Biomedical Imaging, ISBI 2018
EditorsErik Meijering, Ron Summers
Place of PublicationUSA
PublisherIEEE, Institute of Electrical and Electronics Engineers
Number of pages4
ISBN (Electronic)9781538636367
Publication statusPublished - 2018
Externally publishedYes
EventIEEE International Symposium on Biomedical Imaging (ISBI) 2018 - Washington, United States of America
Duration: 4 Apr 20187 Apr 2018
Conference number: 15th

Publication series

NameProceedings - International Symposium on Biomedical Imaging
ISSN (Print)1945-7928
ISSN (Electronic)1945-8452


ConferenceIEEE International Symposium on Biomedical Imaging (ISBI) 2018
Abbreviated titleISBI 2018
Country/TerritoryUnited States of America
Internet address


  • Generative Adversarial Network
  • Nuclei Segmentation
  • Pathology Image Analysis

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