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The purpose of this study is to develop an automated algorithm for thoracicvertebral segmentation on chest radiography using deep learning. 124de-identified lateral chest radiographs on unique patients were obtained.Segmentations of visible vertebrae were manually performed by a medical studentand verified by a board-certified radiologist. 74 images were used fortraining, 10 for validation, and 40 were held out for testing. A U-Net deepconvolutional neural network was employed for segmentation, using the sum ofdice coefficient and binary cross-entropy as the loss function. On the testset, the algorithm demonstrated an average dice coefficient value of 90.5 andan average intersection-over-union (IoU) of 81.75. Deep learning demonstratespromise in the segmentation of vertebrae on lateral chest radiography.

Automated Segmentation of Vertebrae on Lateral Chest Radiography Using Deep Learning