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