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Related Concept Videos

Depth Perception and Spatial Vision

Depth Perception and Spatial Vision

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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Related Experiment Video

Updated: Jun 15, 2025

Robotized Testing of Camera Positions to Determine Ideal Configuration for Stereo 3D Visualization of Open-Heart Surgery

Robotized Testing of Camera Positions to Determine Ideal Configuration for Stereo 3D Visualization of Open-Heart Surgery

Published on: August 12, 2021

Depth estimation from monocular endoscopy using simulation and image transfer approach.

Bong Hyuk Jeong¹, Hang Keun Kim², Young Don Son²

¹Department of Health Sciences and Technology, GAIHST, Gachon University, Incheon, 21999, South Korea.

Computers in Biology and Medicine

|August 23, 2024

Summary

This summary is machine-generated.

This study developed a deep learning method to estimate depth from endoscopic images, overcoming space limitations. The new technique accurately estimates depth, improving endoscopic navigation and clinical outcomes.

Keywords:

Deep learning Depth estimation Endoscopy GAN Simulation-to-real transfer

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Last Updated: Jun 15, 2025

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Area of Science:

Medical Imaging
Computer Vision
Surgical Technology

Background:

Accurate depth perception is vital for endoscopic navigation systems.
Space constraints in endoscopic devices limit the integration of depth cameras.
Current methods for depth estimation in endoscopy face challenges.

Purpose of the Study:

To develop a deep learning model for accurate depth image estimation directly from endoscopic images.
To address the impracticality of incorporating depth cameras into endoscopic systems.
To enhance navigation and clinical outcomes in endoscopy through improved depth information.

Main Methods:

Generated simulated endoscopy images and depth maps using Unity and computed tomography colonography data.
Employed a cycle generative adversarial network (cGAN) to enhance the realism of simulated images.
Trained a deep learning model using synthesized data for depth estimation.

Main Results:

The proposed method demonstrated superior precision in estimating depth images compared to prior unsupervised methods.
Synthesized data using cGAN improved the realism of training images.
The deep learning model achieved accurate depth estimations.

Conclusions:

The developed deep learning approach offers a practical solution for depth estimation in endoscopy.
This method can significantly advance endoscopic navigation and lesion marking.
Improved depth information is expected to lead to better patient outcomes.