Automated Cardiothoracic Ratio Calculation and Cardiomegaly Detection using Deep Learning Approach
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Abstract (arXiv)
We propose an algorithm for calculating the cardiothoracic ratio (CTR) from chest X-ray films. Our approach applies a deep learning model based on U-Net with VGG16 encoder to extract lung and heart masks from chest X-ray images and calculate CTR from the extents of obtained masks. Human radiologists evaluated our CTR measurements, and 76.5% were accepted to be included in medical reports without any need for adjustment. This result translates to a large amount of time and labor saved for radiologists using our automated tools.
Key facts. Cited by 3 papers in the map (data through 14 Oct 2026). First posted on arXiv 18 Feb 2020.
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Citing papers per year: 2021: 2; 2022: 0; 2023: 0; 2024: 0; 2025: 0; 2026: 1.
3 papers.
- 2021Deep Learning Models for Calculation of Cardiothoracic Ratio from Chest Radiographs for Assisted Diagnosis of Cardiomegalycited by 2
- 2021Explaining the Black-box Smoothly- A Counterfactual Approachcited by 1
- 2026X-Ragent: Tool-Augmented Reinforcement Learning for Evidence-Grounded Chest X-Ray Reasoningcited by 0
- 2026X-Ragent: Tool-Augmented Reinforcement Learning for Evidence-Grounded Chest X-Ray Reasoning12 Oct 2026
- 2021Deep Learning Models for Calculation of Cardiothoracic Ratio from Chest Radiographs for Assisted Diagnosis of Cardiomegaly19 Jan 2021
- 2021Explaining the Black-box Smoothly- A Counterfactual Approach11 Jan 2021