Automated Cardiothoracic Ratio Calculation and Cardiomegaly Detection using Deep Learning Approach

TL;DR

This paper presents a deep learning method using U-Net with VGG16 to automatically calculate the cardiothoracic ratio from chest X-rays, achieving high accuracy and saving radiologists time.

Contribution

The study introduces a novel automated approach for CTR calculation using deep learning, validated by radiologists, reducing manual effort and improving efficiency.

Findings

76.5% of automated CTR measurements accepted without adjustment

Significant time and labor savings for radiologists

High accuracy of the deep learning model in clinical evaluation

Abstract

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.