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.

本文提出了一种基于对抗网络的无监督域自适应框架，旨在通过从公开可用的数据源学习域不变特征表示来为未标记的数据集产生准确的胸部器官分割，以协助诊断肺心病。研究结果表明，所提出的模型具有良好的分割性能，并具有临床实用性。