# Unsupervised Domain Adaptation for Automatic Estimation of Cardiothoracic Ratio

arXiv: https://arxiv.org/abs/1807.03434 · HTML: https://mlsanity.com/paper/1807.03434/

## Key facts

- Cited by 16 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 Jul 2018.

## Built on by (most cited first, 16 of 16)

- [Deep Learning for Chest X-ray Analysis: A Survey](https://mlsanity.com/paper/2103.08700/) (2021)
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- [Few-Shot Domain Adaptation with Polymorphic Transformers](https://mlsanity.com/paper/2107.04805/) (2021)
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- [DDPM-MoCo: Advancing Industrial Surface Defect Generation and Detection with Generative and Contrastive Learning](https://mlsanity.com/paper/2407.03332/) (2024)
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- [MaNi: Maximizing Mutual Information for Nuclei Cross-Domain Unsupervised Segmentation](https://mlsanity.com/paper/2206.14437/) (2022)
- [Dual Multi-scale Mean Teacher Network for Semi-supervised Infection Segmentation in Chest CT Volume for COVID-19](https://mlsanity.com/paper/2211.05548/) (2022)
- [DACov: A Deeper Analysis of Data Augmentation on the Computed Tomography Segmentation Problem](https://mlsanity.com/paper/2303.05912/) (2023)
- [CEmb-SAM: Segment Anything Model with Condition Embedding for Joint Learning from Heterogeneous Datasets](https://mlsanity.com/paper/2308.06957/) (2023)
- [Semi-supervised Medical Image Segmentation via Geometry-aware Consistency Training](https://mlsanity.com/paper/2202.06104/) (2022)
- [Multi-organ segmentation: a progressive exploration of learning paradigms under scarce annotation](https://mlsanity.com/paper/2302.03296/) (2023)
- [Explainable Semantic Medical Image Segmentation with Style](https://mlsanity.com/paper/2303.05696/) (2023)
- [Judge Like a Real Doctor: Dual Teacher Sample Consistency Framework for Semi-supervised Medical Image Classification](https://mlsanity.com/paper/2411.03041/) (2024)

## Abstract (arXiv)

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.