  The choroid provides oxygen and nourishment to the outer retina thus is
related to the pathology of various ocular diseases. Optical coherence
tomography (OCT) is advantageous in visualizing and quantifying the choroid in
vivo. (1) The lower boundary of the choroid (choroid-sclera interface) in OCT
is fuzzy, which makes the automatic segmentation difficult and inaccurate. (2)
The visualization of the choroid is hindered by the vessel shadows from the
superficial layers of the inner retina. In this paper, we propose to
incorporate medical and imaging prior knowledge with deep learning to address
these two problems. We propose a biomarker infused global-to-local network for
the choroid segmentation. It leverages the choroidal thickness, a primary
biomarker in clinic, as a constraint to improve the segmentation accuracy. We
also design a global-to-local strategy in the choroid segmentation: a global
module is used to segment all the retinal and choroidal layers simultaneously
for suppressing overfitting and providing global structure information, then a
local module is used to refine the segmentation with the biomarker infusion. To
eliminate the retinal vessel shadows, we propose a pipeline that firstly use
anatomical and OCT imaging knowledge to locate the shadows using their
projection on the retinal pigment epithelium layer, then the contents of the
choroidal vasculature at the shadow locations are predicted with an
edge-to-texture generative adversarial inpainting network. The experiments show
our method outperforms the existing methods on both the segmentation and shadow
elimination tasks. We further apply the proposed method in a clinical
prospective study for understanding the pathology of glaucoma by detecting the
structure and vascular changes of the choroid related to the elevation of
intra-ocular pressure.
