  In this paper we present a methodology that uses convolutional neural
networks (CNNs) for segmentation by iteratively growing predicted mask regions
in each coordinate direction. The CNN is used to predict class probability
scores in a small neighborhood of the center pixel in a tile of an image. We
use a threshold on the CNN probability scores to determine whether pixels are
added to the region and the iteration continues until no new pixels are added
to the region. Our method is able to achieve high segmentation accuracy and
preserve biologically realistic morphological features while leveraging small
amounts of training data and maintaining computational efficiency. Using
retinal blood vessel images from the DRIVE database we found that our method is
more accurate than a fully convolutional semantic segmentation CNN for several
evaluation metrics.
