Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Uniform Depth Channel Flow: Problem Solving01:18

Uniform Depth Channel Flow: Problem Solving

132
To calculate the flow rate for a trapezoidal channel, first, identify the bottom width, side slope, and flow depth of the channel. The cross-sectional area (A) corresponding to the depth of flow (y), channel bottom width (B), and side slope (θ) is determined by:Next, calculate the wetted perimeter, which includes the bottom width and the sloped side lengths in contact with the water. Using the values of the cross-sectional area and the wetted perimeter, determine the hydraulic radius by...
132
Uniform Depth Channel Flow01:27

Uniform Depth Channel Flow

179
Uniform depth channel flow keeps fluid depth consistent along channels such as irrigation canals. In natural channels, such as rivers, approximate uniform flow is often assumed. This condition occurs when the channel’s bottom slope matches the energy slope, balancing potential energy lost from gravity with head loss due to shear stress. This balance prevents depth changes along the channel length, resulting in a steady, uniform flow.Uniform flow in open channels with a constant cross-section...
179

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Correspondence-free local-to-global liver deformation correction via implicit neural representation and biomechanical model.

International journal of computer assisted radiology and surgery·2026
Same author

The role of leptin in reproductive dysfunction in patients with varicocele: a systematic review and meta-analysis.

Frontiers in urology·2026
Same author

2D Ultrasound Elasticity Imaging of Abdominal Aortic Aneurysms Using Deep Neural Networks.

IEEE transactions on computational imaging·2026
Same author

Multi-Omics Analyses of the Gut Microbiota and Metabolism in Cats with Different Body Conditions and the Effects of Fecal Microbiota Transplantation.

Veterinary sciences·2026
Same author

A robust adhesive microneedle for oral infections therapy via synergistic antibacterial and neutrophil-macrophage axis immunomodulation.

Science advances·2026
Same author

Fluorine-Driven Polychromatic Colorimetric Screening of PFAS via Iodide-Mediated Gold Nanorod Etching on a Dual-Ligand Cu-MOF: A "No Aggregation, Low Inhibition" Sensing Paradigm.

Analytical chemistry·2026
Same journal

Toward Patient-Specific Partial Point Cloud to Surface Completion for Pre to Intra-operative Registration in Image-Guided Liver Interventions.

Medical Image Understanding and Analysis. Medical Image Understanding and Analysis (Conference)·2026
Same journal

SCorP: Statistics-Informed Dense Correspondence Prediction Directly from Unsegmented Medical Images.

Medical Image Understanding and Analysis. Medical Image Understanding and Analysis (Conference)·2024
Same journal

M-VAAL: Multimodal Variational Adversarial Active Learning for Downstream Medical Image Analysis Tasks.

Medical Image Understanding and Analysis. Medical Image Understanding and Analysis (Conference)·2024
Same journal

STAMP: A Self-training Student-Teacher Augmentation-Driven Meta Pseudo-Labeling Framework for 3D Cardiac MRI Image Segmentation.

Medical Image Understanding and Analysis. Medical Image Understanding and Analysis (Conference)·2023
Same journal

A Supervised Image Registration Approach for Late Gadolinium Enhanced MRI and Cine Cardiac MRI Using Convolutional Neural Networks.

Medical Image Understanding and Analysis. Medical Image Understanding and Analysis (Conference)·2021
Same journal

Weakly Supervised Learning of Placental Ultrasound Images with Residual Networks.

Medical Image Understanding and Analysis. Medical Image Understanding and Analysis (Conference)·2019
See all related articles

Related Experiment Video

Updated: Sep 22, 2025

Determining 3D Flow Fields via Multi-camera Light Field Imaging
14:25

Determining 3D Flow Fields via Multi-camera Light Field Imaging

Published on: March 6, 2013

16.7K

Dense Depth Estimation from Stereo Endoscopy Videos Using Unsupervised Optical Flow Methods.

Zixin Yang1, Richard Simon2, Yangming Li3

  • 1Center for Imaging Science, Rochester Institute of Technology, Rochester, NY 14623, USA.

Medical Image Understanding and Analysis. Medical Image Understanding and Analysis (Conference)
|May 25, 2022
PubMed
Summary
This summary is machine-generated.

This study introduces END-flow, an unsupervised method for depth estimation in minimally invasive surgery using stereo endoscopy. It overcomes challenges like feature-less surfaces and uncalibrated cameras, achieving performance close to supervised methods.

Keywords:
Depth estimationOptical flowSelf supervised learningStereo endoscopyStereo matching

More Related Videos

Robotized Testing of Camera Positions to Determine Ideal Configuration for Stereo 3D Visualization of Open-Heart Surgery
05:12

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

Published on: August 12, 2021

2.2K
Longitudinal Morphological and Physiological Monitoring of Three-dimensional Tumor Spheroids Using Optical Coherence Tomography
08:50

Longitudinal Morphological and Physiological Monitoring of Three-dimensional Tumor Spheroids Using Optical Coherence Tomography

Published on: February 9, 2019

7.8K

Related Experiment Videos

Last Updated: Sep 22, 2025

Determining 3D Flow Fields via Multi-camera Light Field Imaging
14:25

Determining 3D Flow Fields via Multi-camera Light Field Imaging

Published on: March 6, 2013

16.7K
Robotized Testing of Camera Positions to Determine Ideal Configuration for Stereo 3D Visualization of Open-Heart Surgery
05:12

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

Published on: August 12, 2021

2.2K
Longitudinal Morphological and Physiological Monitoring of Three-dimensional Tumor Spheroids Using Optical Coherence Tomography
08:50

Longitudinal Morphological and Physiological Monitoring of Three-dimensional Tumor Spheroids Using Optical Coherence Tomography

Published on: February 9, 2019

7.8K

Area of Science:

  • Medical Imaging
  • Computer Vision
  • Surgical Technology

Background:

  • Depth estimation from stereo endoscopy is vital for 3D reconstruction, surgical navigation, and augmented reality in Minimally Invasive Surgery (MIS).
  • Key challenges include feature-less surfaces, artifacts, difficulty in obtaining ground truth depth, and uncalibrated camera parameters common in endoscopic procedures.

Purpose of the Study:

  • To propose an unsupervised depth estimation framework, END-flow, to address the limitations of current methods in stereo endoscopic depth estimation.
  • To develop a method that does not require calibrated camera parameters or ground truth depth data.

Main Methods:

  • An unsupervised optical flow network, END-flow, was developed and trained on un-rectified binocular videos.
  • The framework operates without requiring calibrated camera parameters, making it suitable for dynamic endoscopic environments.

Main Results:

  • END-flow was evaluated against traditional stereo matching, self-supervised depth estimation, unsupervised optical flow, and supervised methods on the SCARED dataset.
  • The proposed method demonstrated superior performance compared to several state-of-the-art techniques.
  • END-flow achieved performance comparable to supervised methods, highlighting its effectiveness in unsupervised depth estimation.

Conclusions:

  • The END-flow framework offers a robust solution for unsupervised depth estimation in stereo endoscopy.
  • This approach effectively handles challenges posed by uncalibrated cameras and difficult surface representations in surgical settings.
  • The method shows significant potential for improving 3D reconstruction and navigation in minimally invasive surgery.