Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

You might also read

Related Articles

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

Sort by

Same author

Patient-specific functional liver segments based on centerline classification of the hepatic and portal veins.

Computer assisted surgery (Abingdon, England)·2025

Same author

Extrapolation Convolution for Data Prediction on a 2-D Grid: Bridging Spatial and Frequency Domains With Applications in Image Outpainting and Compressed Sensing.

IEEE transactions on neural networks and learning systems·2025

Same author

EPR25-119: Primary Cardiac Soft Tissue Sarcoma: A Retrospective Analysis.

Journal of the National Comprehensive Cancer Network : JNCCN·2025

Same author

Intraoperative patient-specific volumetric reconstruction and 3D visualization for laparoscopic liver surgery.

Healthcare technology letters·2024

Same author

PolySegNet: improving polyp segmentation through swin transformer and vision transformer fusion.

Biomedical engineering letters·2024

Same author

HoloPatch: improving intracardiac patch fit through holographically modelled templates.

European heart journal. Imaging methods and practice·2024

Same journal

Facial iPPG heatmap patterns based on period-aware autoencoder show association with carotid atherosclerosis towards non-contact hemodynamic assessment.

Computer methods and programs in biomedicine·2026

Same journal

Explainable machine learning models predict liver fibrosis risk and outcome in the general population: Development and multi-cohort external validation.

Computer methods and programs in biomedicine·2026

Same journal

Evaluation of surrogate endpoints for survival outcomes using the surrogate package in R.

Computer methods and programs in biomedicine·2026

Same journal

Relative spectral and frication-based descriptors as numerical indicators of place of articulation shifts in fricatives produced by Polish children.

Computer methods and programs in biomedicine·2026

Same journal

Leaflet resection improves valve expansion and hemodynamic performance in redo TAVI with balloon- and self-expanding transcatheter heart valve configurations.

Computer methods and programs in biomedicine·2026

Same journal

Spectral super-resolution for Parkinson's voice via representation-level methods under mixed-reality acquisition.

Computer methods and programs in biomedicine·2026

See all related articles

Search research articles

Related Experiment Video

Updated: Dec 26, 2025

Rapid Whole-Mount High-Resolution Imaging of Small Animal Vasculature for Quantitative Studies

Rapid Whole-Mount High-Resolution Imaging of Small Animal Vasculature for Quantitative Studies

Published on: May 23, 2025

Fast parallel vessel segmentation.

Nitin Satpute¹, Rabia Naseem², Rafael Palomar³

¹Department of Electronic and Computer Engineering, Universidad de Córdoba, Spain.

Computer Methods and Programs in Biomedicine

|March 15, 2020

Summary

This summary is machine-generated.

This study introduces a faster parallel gradient-based seeded region growing method for segmenting liver vessels in CT scans. The new approach significantly improves speed and accuracy over existing techniques.

Keywords:

GPU Grid-stride loop Kernel termination and relaunch (KTRL)Persistent Seeded region growing

More Related Videos

A Label-Free Segmentation Approach for Intravital Imaging of Mammary Tumor Microenvironment

A Label-Free Segmentation Approach for Intravital Imaging of Mammary Tumor Microenvironment

Published on: May 24, 2022

A Volumetric Method for Quantification of Cerebral Vasospasm in a Murine Model of Subarachnoid Hemorrhage

A Volumetric Method for Quantification of Cerebral Vasospasm in a Murine Model of Subarachnoid Hemorrhage

Published on: July 28, 2018

Related Experiment Videos

Last Updated: Dec 26, 2025

Rapid Whole-Mount High-Resolution Imaging of Small Animal Vasculature for Quantitative Studies

Rapid Whole-Mount High-Resolution Imaging of Small Animal Vasculature for Quantitative Studies

Published on: May 23, 2025

A Label-Free Segmentation Approach for Intravital Imaging of Mammary Tumor Microenvironment

A Label-Free Segmentation Approach for Intravital Imaging of Mammary Tumor Microenvironment

Published on: May 24, 2022

A Volumetric Method for Quantification of Cerebral Vasospasm in a Murine Model of Subarachnoid Hemorrhage

A Volumetric Method for Quantification of Cerebral Vasospasm in a Murine Model of Subarachnoid Hemorrhage

Published on: July 28, 2018

Area of Science:

Medical Imaging
Computer Vision
Computational Anatomy

Background:

Accurate liver vessel segmentation is crucial for clinical applications but remains challenging.
Existing segmentation algorithms are often slow and lack precision.

Purpose of the Study:

To develop a fast and accurate vessel segmentation method for liver slices.
To propose a parallel gradient-based seeded region growing algorithm for enhanced performance.

Main Methods:

Implemented a persistent, grid-stride loop-based parallel seeded region growing approach on GPUs.
Analyzed static regions of interest on GPU tiles to accelerate the segmentation process.

Main Results:

The proposed parallel approach achieved 1.9x greater speed compared to state-of-the-art methods.
Demonstrated faster performance than kernel termination and relaunch techniques.

Conclusions:

The developed parallel seeded region growing method offers a significant speed improvement for liver vessel segmentation.
The algorithm shows high accuracy, outperforming Chan-Vese and Snake models.