  We propose an automated method based on deep learning to compute the
cardiothoracic ratio and detect the presence of cardiomegaly from chest
radiographs. We develop two separate models to demarcate the heart and chest
regions in an X-ray image using bounding boxes and use their outputs to
calculate the cardiothoracic ratio. We obtain a sensitivity of 0.96 at a
specificity of 0.81 with a mean absolute error of 0.0209 on a held-out test
dataset and a sensitivity of 0.84 at a specificity of 0.97 with a mean absolute
error of 0.018 on an independent dataset from a different hospital. We also
compare three different segmentation model architectures for the proposed
method and observe that Attention U-Net yields better results than SE-Resnext
U-Net and EfficientNet U-Net. By providing a numeric measurement of the
cardiothoracic ratio, we hope to mitigate human subjectivity arising out of
visual assessment in the detection of cardiomegaly.
