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

You might also read

Related Articles

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

Sort by
Same author

Efficacy of robot-assisted stereotactic aspiration in moderate basal ganglia hemorrhage: a retrospective cohort study.

Frontiers in surgery·2026
Same author

A multi-stage feature alignment framework with cross-modality collaborative fusion for visible-infrared person re-identification.

Scientific reports·2026
Same author

A critical review on whether reducing the bandgap width is the sole approach to enhancing photocatalytic efficiency in g-C<sub>3</sub>N<sub>4</sub>-based materials.

Environmental research·2026
Same author

Bioinformatic analysis of glycosylation-related genes in intervertebral disc degeneration and their roles in immune infiltration and diagnostic models.

Scientific reports·2025
Same author

WDR4 promotes glioma progression by regulating cell proliferation and cell cycle via the PI3K/Akt-CDK1/2 signaling pathway.

Neoplasma·2025
Same author

A local-global transformer-based model for person re-identification.

PloS one·2025
Same journal

Correction: Jiang et al. Methods for Obtaining One Single Larmor Frequency, Either <i>v</i><sub>1</sub> or <i>v</i><sub>2</sub>, in the Coherent Spin Dynamics of Colloidal Quantum Dots. <i>Nanomaterials</i> 2023, <i>13</i>, 2006.

Nanomaterials (Basel, Switzerland)·2026
Same journal

Correction: Ekman et al. Synthesis, Characterization, and Adsorption Properties of Nitrogen-Doped Nanoporous Biochar: Efficient Removal of Reactive Orange 16 Dye and Colorful Effluents. <i>Nanomaterials</i> 2023, <i>13</i>, 2045.

Nanomaterials (Basel, Switzerland)·2026
Same journal

Ti<sub>3</sub>C<sub>2</sub>T<sub>x</sub>-Based Materials and Coatings for De-Icing and Defogging of Wind Turbine Blades: Materials Basis, Structural Design, Engineering Integration, and Future Opportunities.

Nanomaterials (Basel, Switzerland)·2026
Same journal

Influence of the Ripeness Stages of the Precursors on the Optical Characteristics of Carbon Dots Obtained from Valencia Orange Peels (<i>Citrus sinensis</i> L. Osbeck) by Hydrothermal Synthesis.

Nanomaterials (Basel, Switzerland)·2026
Same journal

Insights into ALD Growth of Al-Based Dielectric Stack on 4H-SiC.

Nanomaterials (Basel, Switzerland)·2026
Same journal

Metal-<i>N</i>-Heterocyclic Carbene Porous Organic Polymers as Efficient Bifunctional Water-Splitting Electrocatalysts.

Nanomaterials (Basel, Switzerland)·2026
See all related articles

Related Experiment Video

Updated: Jan 11, 2026

Resonance Raman Spectroscopy of Extreme Nanowires and Other 1D Systems
07:44

Resonance Raman Spectroscopy of Extreme Nanowires and Other 1D Systems

Published on: April 28, 2016

15.5K

A Wavelet-Based Bilateral Segmentation Study for Nanowires.

Yuting Hou1, Yu Zhang1, Fengfeng Liang1

  • 1School of Computer Science and Technology, Changchun Normal University, Changchun 130032, China.

Nanomaterials (Basel, Switzerland)
|November 12, 2025
PubMed
Summary
This summary is machine-generated.

A new deep learning model, WaveBiSeNet, accurately segments complex one-dimensional (1D) nanowire structures. This advanced image segmentation technique improves analysis for nanomaterials in biosensing and bioelectronics.

Keywords:
BiSeNetV1deep learningfeature extractionone-dimensional nanowiressemantic segmentationwavelet-based convolution

More Related Videos

Ultrahigh Density Array of Vertically Aligned Small-molecular Organic Nanowires on Arbitrary Substrates
08:07

Ultrahigh Density Array of Vertically Aligned Small-molecular Organic Nanowires on Arbitrary Substrates

Published on: June 18, 2013

15.5K
Evaluating Plasmonic Transport in Current-carrying Silver Nanowires
09:00

Evaluating Plasmonic Transport in Current-carrying Silver Nanowires

Published on: December 11, 2013

5.5K

Related Experiment Videos

Last Updated: Jan 11, 2026

Resonance Raman Spectroscopy of Extreme Nanowires and Other 1D Systems
07:44

Resonance Raman Spectroscopy of Extreme Nanowires and Other 1D Systems

Published on: April 28, 2016

15.5K
Ultrahigh Density Array of Vertically Aligned Small-molecular Organic Nanowires on Arbitrary Substrates
08:07

Ultrahigh Density Array of Vertically Aligned Small-molecular Organic Nanowires on Arbitrary Substrates

Published on: June 18, 2013

15.5K
Evaluating Plasmonic Transport in Current-carrying Silver Nanowires
09:00

Evaluating Plasmonic Transport in Current-carrying Silver Nanowires

Published on: December 11, 2013

5.5K

Area of Science:

  • Materials Science
  • Biomedical Engineering
  • Computer Vision

Background:

  • One-dimensional (1D) nanowires are crucial nanomaterials with diverse applications.
  • Accurate segmentation of nanowire morphology is vital for materials science.
  • Dispersed, entangled, and blurred nanowires pose significant segmentation challenges for traditional methods.

Purpose of the Study:

  • To develop an advanced deep learning model for precise segmentation of 1D nanowire images.
  • To overcome limitations of traditional threshold-based segmentation techniques for complex nanowire structures.

Main Methods:

  • Introduction of the Wavelet-based Bilateral Segmentation Network (WaveBiSeNet).
  • Incorporation of a Dual Wavelet Convolution Module (DWCM) for enhanced feature representation.
  • Integration of a Flexible Upsampling Module (FUM) to improve segmentation accuracy.
  • Benchmarking WaveBiSeNet against ten other segmentation models on a peptide nanowire dataset.

Main Results:

  • WaveBiSeNet achieved a mean Intersection over Union (mIoU) of 77.59%.
  • The model demonstrated high accuracy (89.95%), F1 score (87.22%), and Kappa coefficient (74.13%).
  • WaveBiSeNet outperformed ten other advanced segmentation models in performance.

Conclusions:

  • WaveBiSeNet is an effective end-to-end deep segmentation network for complex 1D nanowire structures.
  • The proposed model offers superior segmentation performance compared to existing methods.
  • WaveBiSeNet facilitates accurate analysis of nanomaterials crucial for various technological applications.