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

Boundary Conditions: Lossless Lines01:21

Boundary Conditions: Lossless Lines

482
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...
482
Polymer Classification: Architecture01:14

Polymer Classification: Architecture

2.9K
Polymers are classified as linear or branched on the basis of their chain architecture. The polymer chains in linear polymers have a long chain-like structure with minimal to no branching at all. Even if a polymer features large substituent groups on the monomer, which appear as branches to the skeleton, it is not considered a branched polymer. A branched polymer contains secondary polymer chains that arise from the main polymer chain. The branching occurs when the polymer growth shifts from...
2.9K
Radical Chain-Growth Polymerization: Chain Branching01:17

Radical Chain-Growth Polymerization: Chain Branching

1.8K
The skeletal structure of polymers synthesized via radical polymerization is always branched. For example, the polymerization of ethylene by radical polymerization results in a low-density grade of polyethylene with a heavily branched skeletal structure. Here, the radical site abstracts hydrogen from the growing chain, and the radical site shifts from the end (a primary carbon center) to anywhere within the growing chain (a secondary carbon center). Consequently, the part of the chain from the...
1.8K
Fixation and Sectioning01:03

Fixation and Sectioning

6.0K
Two basic types of preparation are used to visualize specimens with a light microscope: wet mounts and fixed specimens.
The simplest type of preparation is the wet mount, in which the specimen is placed in a drop of liquid on the slide. A liquid specimen can be directly deposited on the slide using a dropper. Solid specimens, such as skin scraping, can be placed on the slide before adding a drop of liquid to prepare the wet mount. Sometimes the liquid is simply water, but stains are often added...
6.0K

You might also read

Related Articles

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

Sort by
Same author

Productivity and quality-related traits of wheat germplasm affected by heat stress.

PloS one·2026
Same author

Multivariate synchrosqueezing transform and time-frequency attention for mental workload classification from EEG signals.

Scientific reports·2026
Same author

Machine learning-based estimation of discharge coefficient for semicircular labyrinth weirs.

Scientific reports·2025
Same author

A novel optimal distributed strategy for time-varying formation tracking control in large-scale robot swarms.

Scientific reports·2025
Same author

Stockwell transform and semi-supervised feature selection from deep features for classification of BCI signals.

Scientific reports·2022
Same author

Time-Frequency Analysis of EEG Signals and GLCM Features for Depth of Anesthesia Monitoring.

Computational intelligence and neuroscience·2021

Related Experiment Video

Updated: Apr 26, 2026

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique
04:48

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

Published on: July 5, 2024

993

SegFormer-based boundary-aware polyp segmentation with adaptive multi-branch fusion.

Mahdi Ouria1, Akbar Asgharzadeh-Bonab2, Hashem Kalbkhani3

  • 1Cognitive Science Research Center, Tehran, Iran.

Scientific Reports
|April 24, 2026
PubMed
Summary
This summary is machine-generated.

This study introduces a SegFormer-based framework for precise colorectal polyp segmentation and edge detection, improving cancer prevention. The AI model achieves high accuracy in defining polyp regions and boundaries across diverse datasets.

Keywords:
Fusion representationPolyp segmentationSegFormerSemantic segmentation

More Related Videos

Automated Joint Space Detection Improves Bone Segmentation Accuracy
06:45

Automated Joint Space Detection Improves Bone Segmentation Accuracy

Published on: November 28, 2025

333
Oncogenic Gene Fusion Detection Using Anchored Multiplex Polymerase Chain Reaction Followed by Next Generation Sequencing
09:49

Oncogenic Gene Fusion Detection Using Anchored Multiplex Polymerase Chain Reaction Followed by Next Generation Sequencing

Published on: July 5, 2019

9.1K

Related Experiment Videos

Last Updated: Apr 26, 2026

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique
04:48

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

Published on: July 5, 2024

993
Automated Joint Space Detection Improves Bone Segmentation Accuracy
06:45

Automated Joint Space Detection Improves Bone Segmentation Accuracy

Published on: November 28, 2025

333
Oncogenic Gene Fusion Detection Using Anchored Multiplex Polymerase Chain Reaction Followed by Next Generation Sequencing
09:49

Oncogenic Gene Fusion Detection Using Anchored Multiplex Polymerase Chain Reaction Followed by Next Generation Sequencing

Published on: July 5, 2019

9.1K

Area of Science:

  • Medical Image Analysis
  • Computer Vision
  • Artificial Intelligence in Healthcare

Background:

  • Accurate colorectal polyp definition is crucial for cancer prevention but challenging due to visual variability and indistinct boundaries.
  • Existing methods struggle with the precise segmentation and boundary delineation of polyps.

Purpose of the Study:

  • To develop and evaluate a SegFormer-aided framework for accurate colorectal polyp segmentation and edge detection.
  • To improve the clinical reliability of polyp definition by enhancing both regional precision and boundary accuracy.

Main Methods:

  • A framework utilizing SegFormer-B4 encoder and a multi-branch fusion head for low-, high-, and all-level feature integration.
  • Optimization using a composite objective including Dice Similarity Coefficient (DSC) loss, Lovász-Hinge loss, and edge-aware Binary Cross-Entropy (BCE).
  • A pre-processing step to remove textual overlays and specular highlights, enhancing image quality for analysis.

Main Results:

  • Achieved excellent region and boundary precision on public datasets: Kvasir-Seg (mDice=0.946, mIoU=0.899), CVC-ClinicDB (mDice=0.961, mIoU=0.926), and ETIS (mDice=0.799, mIoU=0.705).
  • Demonstrated top performance in boundary-sensitive measures (S-measure, weighted F-measure, E-measure).
  • The end-to-end system generalized across datasets without post-processing or external prompting, proving robust performance.

Conclusions:

  • The proposed SegFormer-aided framework significantly enhances the accuracy of colorectal polyp segmentation and boundary definition.
  • The system's ability to generalize and provide precise delineation improves its potential for clinical reliability in cancer prevention.
  • This AI-driven approach offers a promising tool for more accurate polyp analysis in medical imaging.