Shape Mask Generator: Learning to Refine Shape Priors for Segmenting Overlapping Cervical Cytoplasms

Youyi Song, Lei Zhu, Baiying Lei, Bin Sheng, Qi Dou, Jing Qin, Kup Sze Choi

Research output: Chapter in book / Conference proceedingConference article published in proceeding or bookAcademic researchpeer-review

2 Citations (Scopus)

Abstract

Segmenting overlapping cytoplasm of cervical cells plays a crucial role in cervical cancer screening. This task, however, is rather challenging, mainly because intensity (or color) information in the overlapping region is deficient for recognizing occluded boundary parts. Existing methods attempt to compensate intensity deficiency by exploiting shape priors, but shape priors modeled by them have a weak representation ability, and hence their segmentation results are often visually implausible in shape. In this paper, we propose a conceptually simple and effective technique, called shape mask generator, for segmenting overlapping cytoplasms. The key idea is to progressively refine shape priors by learning so that they can accurately represent most cytoplasms’ shape. Specifically, we model shape priors from shape templates and feed them to the shape mask generator that generates a shape mask for the cytoplasm as the segmentation result. Shape priors are refined by minimizing the ‘generating residual’ in the training dataset, which is designed to have a smaller value when the shape mask generator producing shape masks that are more consistent with the image information. The introduced method is assessed on two datasets, and the empirical evidence shows that it is effective, outperforming existing methods.

Original languageEnglish
Title of host publicationMedical Image Computing and Computer Assisted Intervention – MICCAI 2020 - 23rd International Conference, Proceedings
EditorsAnne L. Martel, Purang Abolmaesumi, Danail Stoyanov, Diana Mateus, Maria A. Zuluaga, S. Kevin Zhou, Daniel Racoceanu, Leo Joskowicz
PublisherSpringer Science and Business Media Deutschland GmbH
Pages639-649
Number of pages11
ISBN (Print)9783030597184
DOIs
Publication statusPublished - Sep 2020
Event23rd International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2020 - Lima, Peru
Duration: 4 Oct 20208 Oct 2020

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume12264 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference23rd International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2020
Country/TerritoryPeru
CityLima
Period4/10/208/10/20

Keywords

  • Cervical cancer screening
  • Overlapping cytoplasms segmentation
  • Refining shape priors
  • Shape mask generator

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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