  Retinopathy of Prematurity (ROP) is an eye disorder primarily affecting
premature infants with lower weights. It causes proliferation of vessels in the
retina and could result in vision loss and, eventually, retinal detachment,
leading to blindness. While human experts can easily identify severe stages of
ROP, the diagnosis of earlier stages, which are the most relevant to
determining treatment choice, are much more affected by variability in
subjective interpretations of human experts. In recent years, there has been a
significant effort to automate the diagnosis using deep learning. This paper
builds upon the success of previous models and develops a novel architecture,
which combines object segmentation and convolutional neural networks (CNN) to
construct an effective classifier of ROP stages 1-3 based on neonatal retinal
images. Motivated by the fact that the formation and shape of a demarcation
line in the retina is the distinguishing feature between earlier ROP stages,
our proposed system first trains an object segmentation model to identify the
demarcation line at a pixel level and adds the resulting mask as an additional
"color" channel in the original image. Then, the system trains a CNN classifier
based on the processed images to leverage information from both the original
image and the mask, which helps direct the model's attention to the demarcation
line. In a number of careful experiments comparing its performance to previous
object segmentation systems and CNN-only systems trained on our dataset, our
novel architecture significantly outperforms previous systems in accuracy,
demonstrating the effectiveness of our proposed pipeline.
