  Vessel segmentation of retinal images is a key diagnostic capability in
ophthalmology. This problem faces several challenges including low contrast,
variable vessel size and thickness, and presence of interfering pathology such
as micro-aneurysms and hemorrhages. Early approaches addressing this problem
employed hand-crafted filters to capture vessel structures, accompanied by
morphological post-processing. More recently, deep learning techniques have
been employed with significantly enhanced segmentation accuracy. We propose a
novel domain enriched deep network that consists of two components: 1) a
representation network that learns geometric features specific to retinal
images, and 2) a custom designed computationally efficient residual task
network that utilizes the features obtained from the representation layer to
perform pixel-level segmentation. The representation and task networks are {\em
jointly learned} for any given training set. To obtain physically meaningful
and practically effective representation filters, we propose two new
constraints that are inspired by expected prior structure on these filters: 1)
orientation constraint that promotes geometric diversity of curvilinear
features, and 2) a data adaptive noise regularizer that penalizes false
positives. Multi-scale extensions are developed to enable accurate detection of
thin vessels. Experiments performed on three challenging benchmark databases
under a variety of training scenarios show that the proposed prior guided deep
network outperforms state of the art alternatives as measured by common
evaluation metrics, while being more economical in network size and inference
time.
