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

BioNet: Infusing Biomarker Prior into Global-to-Local Network for Choroid Segmentation in Optical Coherence Tomography Images