Unsupervised Domain Adaptation for Automatic Estimation of Cardiothoracic Ratio

  • Nanqing DongEmail author
  • Michael Kampffmeyer
  • Xiaodan Liang
  • Zeya Wang
  • Wei Dai
  • Eric Xing
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11071)


The cardiothoracic ratio (CTR), a clinical metric of heart size in chest X-rays (CXRs), is a key indicator of cardiomegaly. Manual measurement of CTR is time-consuming and can be affected by human subjectivity, making it desirable to design computer-aided systems that assist clinicians in the diagnosis process. Automatic CTR estimation through chest organ segmentation, however, requires large amounts of pixel-level annotated data, which is often unavailable. To alleviate this problem, we propose an unsupervised domain adaptation framework based on adversarial networks. The framework learns domain invariant feature representations from openly available data sources to produce accurate chest organ segmentation for unlabeled datasets. Specifically, we propose a model that enforces our intuition that prediction masks should be domain independent. Hence, we introduce a discriminator that distinguishes segmentation predictions from ground truth masks. We evaluate our system’s prediction based on the assessment of radiologists and demonstrate the clinical practicability for the diagnosis of cardiomegaly. We finally illustrate on the JSRT dataset that the semi-supervised performance of our model is also very promising.



We thank Wingspan Technology for collecting and annotating the data for this study.


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Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  • Nanqing Dong
    • 1
    • 2
    Email author
  • Michael Kampffmeyer
    • 3
  • Xiaodan Liang
    • 4
  • Zeya Wang
    • 1
  • Wei Dai
    • 1
  • Eric Xing
    • 1
  1. 1.Petuum, Inc.PittsburghUSA
  2. 2.Cornell UniversityIthacaUSA
  3. 3.UiT The Arctic University of NorwayTromsøNorway
  4. 4.Carnegie Mellon UniversityPittsburghUSA

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