Abstract
The decreasing costs and increasing efficiency of medical imaging equipment has made radiology a central tool in diagnosing diseases all around the world. This has created an opportunity to develop efficient computer vision algorithms with a large potential impact. Two segmentation models based on the U-Net architecture were trained on publicly available and private datasets, obtained from collaborating hospitals.
They achieved good performance (0.989 and 0.983 using the DICE metric), that generalizes to new data distributions, on the task of automatically calculating the cardiothoracic ratio from frontal radiographies. When testing on previously unseen datasets with new data distributions to measure their ability to generalize we obtain average DICE scores of 0.968 and 0.963 respectively. This ratio can be used as a clear indicator of the abnormal enlargement of the heart, and can be linked with other chest pathologies.
The algorithm was tested in collaboration with a team of 3 radiologists, in a real world hospital setting. On average, the time they spent analyzing the heart was decreased, from 22.53s to 5.14s, and their F1 score on the task of cardiomegaly detection was increased from 0.845 to 0.851. Localized energybased normalization was applied and an increase in generalization ability of the model was observed, which was tested using 5 fold cross validation on all of the datasets.
We propose studying how lung and heart segmentation can be used to detect structural abnormalities.
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