  Automatic blood vessel segmentation from retinal images plays an important
role in the diagnosis of many systemic and eye diseases, including retinopathy
of prematurity. Current state-of-the-art research in blood vessel segmentation
from retinal images is based on convolutional neural networks. The solutions
proposed so far are trained and tested on images from a few available retinal
blood vessel segmentation datasets, which might limit their performance when
given an image with retinopathy of prematurity signs. In this paper, we
evaluate the performance of three high-performing convolutional neural networks
for retinal blood vessel segmentation in the context of blood vessel
segmentation on retinopathy of prematurity retinal images. The main motive
behind the study is to test if existing public datasets suffice to develop a
high-performing predictor that could assist an ophthalmologist in retinopathy
of prematurity diagnosis. To do so, we create a dataset consisting solely of
retinopathy of prematurity images with retinal blood vessel annotations
manually labeled by two observers, where one is the ophthalmologist experienced
in retinopathy of prematurity treatment. Experimental results show that all
three solutions have difficulties in detecting the retinal blood vessels of
infants due to a lower contrast compared to images from public datasets as
demonstrated by a significant drop in classification sensitivity. All three
solutions segment alongside retinal also choroidal blood vessels which are not
used to diagnose retinopathy of prematurity, but instead represent noise and
are confused with retinal blood vessels. By visual and numerical observations,
we observe that existing solutions for retinal blood vessel segmentation need
improvement toward more detailed datasets or deeper models in order to assist
the ophthalmologist in retinopathy of prematurity diagnosis.
