  Over the last decade, convolutional neural networks (CNNs) have emerged as
the leading algorithms in image classification and segmentation. Recent
publication of large medical imaging databases have accelerated their use in
the biomedical arena. While training data for photograph classification
benefits from aggressive geometric augmentation, medical diagnosis --
especially in chest radiographs -- depends more strongly on feature location.
Diagnosis classification results may be artificially enhanced by reliance on
radiographic annotations. This work introduces a general pre-processing step
for chest x-ray input into machine learning algorithms. A modified Y-Net
architecture based on the VGG11 encoder is used to simultaneously learn
geometric orientation (similarity transform parameters) of the chest and
segmentation of radiographic annotations. Chest x-rays were obtained from
published databases. The algorithm was trained with 1000 manually labeled
images with augmentation. Results were evaluated by expert clinicians, with
acceptable geometry in 95.8% and annotation mask in 96.2% (n=500), compared to
27.0% and 34.9% respectively in control images (n=241). We hypothesize that
this pre-processing step will improve robustness in future diagnostic
algorithms.
