  Choroid is the vascular layer of the eye, which is directly related to the
incidence and severity of many ocular diseases. Optical Coherence Tomography
(OCT) is capable of imaging both the cross-sectional view of retina and
choroid, but the segmentation of the choroid region is challenging because of
the fuzzy choroid-sclera interface (CSI). In this paper, we propose a biomarker
infused global-to-local network (BioNet) for choroid segmentation, which
segments the choroid with higher credibility and robustness. Firstly, our
method trains a biomarker prediction network to learn the features of the
biomarker. Then a global multi-layers segmentation module is applied to segment
the OCT image into 12 layers. Finally, the global multi-layered result and the
original OCT image are fed into a local choroid segmentation module to segment
the choroid region with the biomarker infused as regularizer. We conducted
comparison experiments with the state-of-the-art methods on a dataset (named
AROD). The experimental results demonstrate the superiority of our method with
$90.77\%$ Dice-index and 6.23 pixels Average-unsigned-surface-detection-error,
etc.
