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

Reducing Line Loss01:18

Reducing Line Loss

193
In a three-phase circuit, line loss is an indicator of energy dissipated as heat due to the resistance of transmission lines. To address this, incorporating transformers into the system—a step-up transformer at the source and a step-down transformer at the load—is a strategic solution. Two three-phase transformers are introduced to improve this.
With a step-up transformer at the source, the voltage is increased, thereby reducing the current in the transmission lines since power loss...
193
Topographic Surveying and Contours01:29

Topographic Surveying and Contours

253
Topographic surveying is critical for documenting the Earth's surface, focusing on capturing elevations, slopes, and natural and man-made features. It is essential in construction planning, water resource management, and land-use analysis. The primary outcome of such surveys is a topographic map, which uses contour lines to visually represent the shape and slope of the terrain, providing valuable insights into the landscape's characteristics.Contour lines are fundamental to understanding the...
253
Uniform Depth Channel Flow: Problem Solving01:18

Uniform Depth Channel Flow: Problem Solving

125
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...
125
Boundary Conditions: Lossless Lines01:21

Boundary Conditions: Lossless Lines

152
Consider a single-phase, two-wire, lossless transmission line terminated by an impedance at the receiving end and a source with Thevenin voltage and impedance at the sending end. The line, with length, has a surge impedance and wave velocity determined by the line's inductance and capacitance.
At the receiving end, the boundary condition states that the voltage equals the product of the receiving-end impedance and current. This relationship is expressed as a function of the incident and...
152
Design Example: Analyzing Capacity Contours for Flood Risk Assessment01:17

Design Example: Analyzing Capacity Contours for Flood Risk Assessment

101
Flood risk assessment involves careful planning and analysis to ensure the safety of communities near water retention structures. Capacity contours are a vital tool in this process, as they illustrate the potential spread of water at specific levels in a given area. In the context of building a bund across a small valley, these contours play a critical role in evaluating the safety of nearby residential areas.In this example, the bund is intended to store stormwater in the valley. The engineers...
101
Deconvolution01:20

Deconvolution

251
Deconvolution, also known as inverse filtering, is the process of extracting the impulse response from known input and output signals. This technique is vital in scenarios where the system's characteristics are unknown, and they must be inferred from the observable signals.
Deconvolution involves several mathematical techniques to derive the impulse response. One common approach is polynomial division. In this method, the input and output sequences are treated as coefficients of...
251

You might also read

Related Articles

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

Sort by
Same author

From anatomy to vulnerability: A comprehensive appraisal of coronary physiology and plaque characterization.

American heart journal plus : cardiology research and practice·2026
Same author

Murray law-based quantitative flow ratio versus invasive fractional flow reserve in the left anterior descending coronary artery: impact of hydrostatic pressure correction and clinical implications.

Frontiers in cardiovascular medicine·2026
Same author

Histological validation of artificial intelligence-driven automatic plaque characterization in coronary OCT: a head-to-head comparison with clinicians.

Cardiology plus·2026
Same author

Dual-domain radio-frequency signal and image joint modeling for coronary calcium detection in intravascular ultrasound.

Medical & biological engineering & computing·2026
Same author

Angiographic Quantitative Flow Ratio-Guided Coronary Intervention: 5-Year Follow-Up From the FAVOR III China Randomized Trial.

Journal of the American College of Cardiology·2026
Same author

Reply: Radial Wall Strain and the Stepwise Integration of Physiology and Vulnerability in Revascularization Decision Making.

JACC. Asia·2026

Related Experiment Video

Updated: Sep 10, 2025

Automated Midline Shift and Intracranial Pressure Estimation based on Brain CT Images
14:08

Automated Midline Shift and Intracranial Pressure Estimation based on Brain CT Images

Published on: April 13, 2013

42.8K

Difficulty-aware coupled contour regression network with IoU loss for efficient IVUS delineation.

Yuan Yang1, Xu Yu1, Wei Yu2

  • 1School of Biomedical Engineering, Southern Medical University, Guangzhou, 510515, China; Guangdong Provincial Key Laboratory of Medical Image Processing, Southern Medical University, Guangzhou, 510515, China.

Artificial Intelligence in Medicine
|August 21, 2025
PubMed
Summary

This study introduces a novel contour regression method for intravascular ultrasound (IVUS) imaging, improving lumen and external elastic lamina delineation. The approach achieves high accuracy and anatomical plausibility, even in images with artifacts.

Keywords:
Contour regressionDeep learningIntravascular ultrasoundSegmentation

More Related Videos

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography
04:48

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Published on: November 30, 2022

2.9K
Intravascular Ultrasound Image-Based Finite Element Modeling Approach for Quantifying In Vivo Mechanical Properties of Human Coronary Artery
06:18

Intravascular Ultrasound Image-Based Finite Element Modeling Approach for Quantifying In Vivo Mechanical Properties of Human Coronary Artery

Published on: December 6, 2024

699

Related Experiment Videos

Last Updated: Sep 10, 2025

Automated Midline Shift and Intracranial Pressure Estimation based on Brain CT Images
14:08

Automated Midline Shift and Intracranial Pressure Estimation based on Brain CT Images

Published on: April 13, 2013

42.8K
Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography
04:48

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Published on: November 30, 2022

2.9K
Intravascular Ultrasound Image-Based Finite Element Modeling Approach for Quantifying In Vivo Mechanical Properties of Human Coronary Artery
06:18

Intravascular Ultrasound Image-Based Finite Element Modeling Approach for Quantifying In Vivo Mechanical Properties of Human Coronary Artery

Published on: December 6, 2024

699

Area of Science:

  • Medical Imaging
  • Image Analysis
  • Cardiovascular Technology

Background:

  • Accurate delineation of lumen and external elastic lamina in intravascular ultrasound (IVUS) images is vital for quantitative analysis.
  • Artifacts in IVUS images present significant challenges for existing segmentation and contour delineation methods.
  • Current mask-based methods can yield implausible contours, while contour-based methods struggle with over-smoothing in artifact regions.

Purpose of the Study:

  • To develop a robust contour regression framework for accurate lumen and external elastic lamina delineation in IVUS images.
  • To address limitations of existing methods in handling image artifacts and anatomical implausibility.
  • To improve the accuracy and efficiency of quantitative analysis in IVUS imaging.

Main Methods:

  • Directly regressing contour pairs using a coupled contour representation to learn a low-dimensional signature space.
  • Employing a proposed PIoU loss function to enhance similarity with manually delineated contours, addressing irregular shapes.
  • Implementing a difficulty-aware training strategy to improve contour localization accuracy in images with severe artifacts.

Main Results:

  • Achieved mean Dice similarity coefficients of 0.951 for lumen and 0.967 for external elastic lamina on a large IVUS dataset.
  • Demonstrated superior performance compared to state-of-the-art (SOTA) models, with all regressed contours being anatomically plausible.
  • Attained comparable performance to SOTA models on the IVUS-2011 dataset with a high processing speed of 100 fps.

Conclusions:

  • The proposed direct contour regression method effectively delineates lumen and external elastic lamina in IVUS images, outperforming existing techniques.
  • The framework successfully handles image artifacts and ensures anatomical plausibility, enhancing quantitative analysis reliability.
  • The method offers a significant advancement in IVUS image analysis, providing both high accuracy and processing speed.