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Related Experiment Video

Updated: Sep 10, 2025

High-resolution, High-speed, Three-dimensional Video Imaging with Digital Fringe Projection Techniques

High-resolution, High-speed, Three-dimensional Video Imaging with Digital Fringe Projection Techniques

Published on: December 3, 2013

Deep-learning-based endoscopic single-shot fringe projection profilometry.

Ruizhi Zuo¹, Shuwen Wei¹, Yaning Wang¹

¹Johns Hopkins University, Department of Electrical and Computer Engineering, Baltimore, Maryland, United States.

Journal of Biomedical Optics

|August 21, 2025

Summary

This summary is machine-generated.

A new single-shot fringe projection profilometry (FPP) system uses deep learning for real-time depth map generation. This endoscopic approach enhances surgical guidance accuracy and speed.

Keywords:

3D optical imaging deep learning endoscope fringe projection profilometry surgery guidance

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Last Updated: Sep 10, 2025

High-resolution, High-speed, Three-dimensional Video Imaging with Digital Fringe Projection Techniques

High-resolution, High-speed, Three-dimensional Video Imaging with Digital Fringe Projection Techniques

Published on: December 3, 2013

Conducting Multiple Imaging Modes with One Fluorescence Microscope

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Published on: October 28, 2018

Rapid Acquisition of 3D Images Using High-resolution Episcopic Microscopy

Rapid Acquisition of 3D Images Using High-resolution Episcopic Microscopy

Published on: November 21, 2016

Area of Science:

Medical Imaging
Robotics
Computer Vision

Background:

Conventional fringe projection profilometry (FPP) is too slow for dynamic surgical measurements due to multiple image acquisitions.
High-speed, accurate depth map generation is crucial for real-time robotic surgical guidance.

Purpose of the Study:

To develop and demonstrate a deep-learning-based, single-shot FPP system for endoscopic surgical guidance.
To achieve real-time, accurate depth map generation of target tissues.

Main Methods:

An endoscopic single-shot FPP system using a dual-channel endoscope was designed.
A deep learning network, combining MaskNet for segmentation and DepthNet for depth prediction, was developed.
A data synthesis method was employed to create diverse training datasets.

Main Results:

The system achieved a maximum depth prediction error of approximately 2 mm.
Processing time per frame was around 12.75 ms.
An optimal fringe pattern frequency of 20 Hz was identified for the single-shot FPP setup.

Conclusions:

The deep-learning-based single-shot FPP endoscopic system effectively generates real-time depth maps with millimeter-scale accuracy.
This technology has the potential to significantly improve the reliability of image-guided robotic surgery.