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

A comparative assessment of [99mTc] HYNIC-PSMA-11 scintigraphy in prostate cancer as a potential substitute for disease evaluation with [68Ga] PSMA-11 PET/CT imaging.

Nuclear medicine communications·2026

Same author

Use of cyclosporine in acute severe ulcerative colitis with low blood cholesterol level: An early experience.

Indian journal of gastroenterology : official journal of the Indian Society of Gastroenterology·2026

Same author

Autism-like phenotypes and increased NMDAR2D expression in mice with KDM5B histone lysine demethylase deficiency.

Science advances·2026

Same author

Mycophenolic Acid Exposure in Lupus Nephritis: Correlation With Therapeutic Response and Adverse Events to Standard Clinical Dose.

Therapeutic drug monitoring·2026

Same author

Efficacy and Safety of Desidustat Versus Erythropoietin in Post Kidney Transplant Anaemia: A Prospective Observational Study.

Nephrology (Carlton, Vic.)·2026

Same author

Diagnostic Value of Multiparametric Transrectal Ultrasound in Patients with Suspected Carcinoma of Prostate: A Tertiary Care Centre Experience.

The Indian journal of radiology & imaging·2026

Same journal

RETRACTION: Real-Time Modulation of Physical Training Intensity Based on Wavelet Recursive Fuzzy Neural Networks.

Computational intelligence and neuroscience·2026

Same journal

RETRACTION: Multidimensional Heterogeneous Network Link Adaptation Based on Mobile Environment.

Computational intelligence and neuroscience·2026

Same journal

RETRACTION: Framework to Segment and Evaluate Multiple Sclerosis Lesion in MRI Slices Using VGG-UNet.

Computational intelligence and neuroscience·2026

Same journal

RETRACTION: Facial Emotion Recognition Using a Novel Fusion of Convolutional Neural Network and Local Binary Pattern in Crime Investigation.

Computational intelligence and neuroscience·2026

Same journal

RETRACTION: Automatic Intelligent System Using Medical of Things for Multiple Sclerosis Detection.

Computational intelligence and neuroscience·2026

Same journal

RETRACTION: Intangible Cultural Heritage Reproduction and Revitalization: Value Feedback, Practice, and Exploration Based on the IPA Model.

Computational intelligence and neuroscience·2026

See all related articles

Search research articles

Related Experiment Video

Updated: Aug 25, 2025

LeafJ: An ImageJ Plugin for Semi-automated Leaf Shape Measurement

LeafJ: An ImageJ Plugin for Semi-automated Leaf Shape Measurement

Published on: January 21, 2013

U-DAVIS-Deep Learning Based Arm Venous Image Segmentation Technique for Venipuncture.

Avik Kuthiala¹, Naman Tuli¹, Harpreet Singh¹

¹Thapar Institute of Engineering and Technology, Patiala 147001, India.

Computational Intelligence and Neuroscience

|October 14, 2022

Summary

This summary is machine-generated.

This study presents a new dataset of arm images for vein segmentation, crucial for improving medical vein location. A U-Net model trained on this dataset demonstrates effective vein identification, aiding in smarter venipuncture.

More Related Videos

Reliable Isolation of Central Nervous System Microvessels Across Five Vertebrate Groups

Reliable Isolation of Central Nervous System Microvessels Across Five Vertebrate Groups

Published on: January 12, 2020

Microperfusion Technique to Investigate Regulation of Microvessel Permeability in Rat Mesentery

Microperfusion Technique to Investigate Regulation of Microvessel Permeability in Rat Mesentery

Published on: September 12, 2015

Related Experiment Videos

Last Updated: Aug 25, 2025

LeafJ: An ImageJ Plugin for Semi-automated Leaf Shape Measurement

LeafJ: An ImageJ Plugin for Semi-automated Leaf Shape Measurement

Published on: January 21, 2013

Reliable Isolation of Central Nervous System Microvessels Across Five Vertebrate Groups

Reliable Isolation of Central Nervous System Microvessels Across Five Vertebrate Groups

Published on: January 12, 2020

Microperfusion Technique to Investigate Regulation of Microvessel Permeability in Rat Mesentery

Microperfusion Technique to Investigate Regulation of Microvessel Permeability in Rat Mesentery

Published on: September 12, 2015

Area of Science:

Medical Imaging
Computer Vision
Biomedical Engineering

Background:

Accurate vein localization is essential for successful intravenous procedures.
Traditional methods for vein identification can be challenging and time-consuming.
Computer vision offers a promising approach to enhance vein imaging and detection.

Purpose of the Study:

To curate and present a high-resolution dataset of arm images for vein segmentation.
To develop and evaluate an image segmentation model for accurate vein identification.
To explore potential applications of the dataset in medical procedures.

Main Methods:

Near-infrared imaging was used to capture high-resolution arm images under ambient lighting.
Images were annotated to create corresponding segmentation masks.
A U-Net based model was trained using preprocessing and augmentation techniques.
Segmentation results were evaluated using various metrics and visualizations.

Main Results:

A novel, high-resolution arm vein image dataset was successfully created and annotated.
The U-Net model achieved effective image segmentation for vein identification.
Comparative analysis demonstrated the performance of the segmentation model.
The study confirmed the usability and potential applications of the dataset.

Conclusions:

The presented dataset and U-Net based segmentation model show significant potential for improving smart venipuncture.
This work facilitates advancements in computer-aided vein detection for medical applications.
The dataset serves as a valuable resource for further research in medical image analysis.