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The cardiothoracic ratio (CTR), a clinical metric of heart size in chestX-rays (CXRs), is a key indicator of cardiomegaly. Manual measurement of CTR istime-consuming and can be affected by human subjectivity, making it desirableto design computer-aided systems that assist clinicians in the diagnosisprocess. Automatic CTR estimation through chest organ segmentation, however,requires large amounts of pixel-level annotated data, which is oftenunavailable. To alleviate this problem, we propose an unsupervised domainadaptation framework based on adversarial networks. The framework learns domaininvariant feature representations from openly available data sources to produceaccurate chest organ segmentation for unlabeled datasets. Specifically, wepropose a model that enforces our intuition that prediction masks should bedomain independent. Hence, we introduce a discriminator that distinguishessegmentation predictions from ground truth masks. We evaluate our system'sprediction based on the assessment of radiologists and demonstrate the clinicalpracticability for the diagnosis of cardiomegaly. We finally illustrate on theJSRT dataset that the semi-supervised performance of our model is also verypromising.

Unsupervised Domain Adaptation for Automatic Estimation of Cardiothoracic Ratio