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Depth Perception and Spatial Vision

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Depth perception is the ability to perceive objects three-dimensionally. It relies on two types of cues: binocular and monocular. Binocular cues depend on the combination of images from both eyes and how the eyes work together. Since the eyes are in slightly different positions, each eye captures a slightly different image. This disparity between images, known as binocular disparity, helps the brain interpret depth. When the brain compares these images, it determines the distance to an object.

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Automatic Segmentation and Visualization of Choroid in OCT with Knowledge Infused Deep Learning.

Huihong Zhang, Jianlong Yang, Kang Zhou

IEEE Journal of Biomedical and Health Informatics

|September 15, 2020

Summary

This study introduces Bio-Net, a deep learning method to improve choroid imaging using optical coherence tomography (OCT). It enhances visualization and segmentation of the choroid for better ocular disease research.

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Area of Science:

Ophthalmology
Medical Imaging
Artificial Intelligence

Background:

The choroid is vital for retinal health, but its study via optical coherence tomography (OCT) is limited by fuzzy boundaries and retinal vessel shadows.
Accurate choroidal imaging is crucial for understanding ocular diseases like glaucoma.

Purpose of the Study:

To develop advanced deep learning methods for overcoming limitations in OCT-based choroid visualization and segmentation.
To improve the accuracy of choroid segmentation and remove retinal vessel shadows for enhanced in vivo analysis.

Main Methods:

A biomarker-infused global-to-local network (Bio-Net) was developed for choroid segmentation, incorporating choroid thickness priors and a global-to-local strategy.
A deep learning pipeline was created to eliminate retinal vessel shadows by predicting vascular contents using a generative adversarial inpainting network.

Main Results:

The proposed Bio-Net method significantly improved choroid segmentation accuracy compared to existing techniques.
The deep learning pipeline effectively removed retinal vessel shadows, enhancing choroidal vasculature visualization.
The method demonstrated utility in a clinical study for detecting choroidal changes associated with glaucoma and intra-ocular pressure.

Conclusions:

The developed deep learning approach enhances OCT imaging of the choroid, addressing key segmentation and visualization challenges.
This technology offers a powerful tool for in vivo research into ocular pathologies, including glaucoma, by enabling detailed structural and vascular analysis of the choroid.