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

Deep Learning Models for Calculation of Cardiothoracic Ratio from Chest Radiographs for Assisted Diagnosis of Cardiomegaly