Unsupervised Learning for CT Image Segmentation via Adversarial Redrawing

Youyi Song, Teng Zhou, Jeremy Yuen Chun Teoh, Jing Zhang, Jing Qin

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

6 Citations (Scopus)

Abstract

We propose a novel adversarial learning framework for unsupervised training of CNNs in CT image segmentation. It is motivated by difficulties in collecting voxel-wise annotations, which is laborious, time-consuming and expensive. It is conceptually simple, allowing us to train an effective segmentation network without any human annotation. Specifically, we design the generator with a CNN producing the segmentation results and a decoder redrawing the CT volume based on the segmentation results. The CNN is then implicitly trained in the adversarial learning framework where a discriminator gradually enforcing the generator to generate CT volumes whose distribution well matches the distribution of the training data. We further propose two constrains as regularization schemes for the training procedure to drive the model towards optimal segmentation by avoiding some unreasonable results. We conducted extensive experiments to evaluate the proposed method on a famous publicly available dataset, and the experimental results demonstrate the effectiveness of the proposed method.

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
Pages309-320
Number of pages12
ISBN (Print)9783030597184
DOIs
Publication statusPublished - 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

  • Adversarial redrawing
  • CNNs
  • CT image segmentation
  • Unsupervised learning

ASJC Scopus subject areas

  • Theoretical Computer Science
  • General Computer Science

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