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

Copper-mediated amidation of alkenylzirconocenes with acyl azides: formation of enamides.

Organic letters·2013
Same author

JARID1A, JMY, and PTGER4 polymorphisms are related to ankylosing spondylitis in Chinese Han patients: a case-control study.

PloS one·2013
Same author

[The risk factors of ventilator-associated pneumonia in newborn and the changes of isolated pathogens].

Sichuan da xue xue bao. Yi xue ban = Journal of Sichuan University. Medical science edition·2013
Same author

A route to phase controllable Cu2ZnSn(S(1-x)Se(x))4 nanocrystals with tunable energy bands.

Scientific reports·2013
Same author

Efficacy of an infection control program in reducing ventilator-associated pneumonia in a Chinese neonatal intensive care unit.

American journal of infection control·2013
Same author

[Effect of different forms of inorganic nitrogen on the photodegradation of antipyrine in water].

Huan jing ke xue= Huanjing kexue·2013
Same journal

Non-contact Heart Sound Measurement by Defocused Speckle Imaging.

IEEE journal of biomedical and health informatics·2026
Same journal

TaxEL: Taxonomy-Enhanced Entity Representation Learning for Biomedical Entity Linking.

IEEE journal of biomedical and health informatics·2026
Same journal

Multimodal Feature Prototype Learning for Interpretable and Discriminative Cancer Survival Prediction.

IEEE journal of biomedical and health informatics·2026
Same journal

CrossSG-DTA: Synergizing Sequence Semantics and Graph Structures via Cross-Attention for Drug-Target Affinity Prediction.

IEEE journal of biomedical and health informatics·2026
Same journal

FGCSA-Net: A Novel Framework for Medical Report Generation Via Fine-Grained Feature Preservation and Semantic Alignment.

IEEE journal of biomedical and health informatics·2026
Same journal

Med-SORA: Symptom to Organ Reasoning in Abdomen CT Images.

IEEE journal of biomedical and health informatics·2026
See all related articles

Related Experiment Video

Updated: Jan 16, 2026

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

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

Published on: July 5, 2024

735

Text-Driven Weakly Supervised OCT Lesion Segmentation With Structural Guidance.

Jiaqi Yang, Nitish Mehta, Xiaoling Hu

    IEEE Journal of Biomedical and Health Informatics
    |October 1, 2025
    PubMed
    Summary
    This summary is machine-generated.

    This study introduces a novel weakly supervised semantic segmentation framework using image-level labels for Optical Coherence Tomography (OCT) lesion segmentation. It integrates structural and text-driven guidance to improve diagnostic accuracy in retinal diseases.

    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

    3.3K
    Lesion Explorer: A Video-guided, Standardized Protocol for Accurate and Reliable MRI-derived Volumetrics in Alzheimer's Disease and Normal Elderly
    12:50

    Lesion Explorer: A Video-guided, Standardized Protocol for Accurate and Reliable MRI-derived Volumetrics in Alzheimer's Disease and Normal Elderly

    Published on: April 14, 2014

    40.8K

    Related Experiment Videos

    Last Updated: Jan 16, 2026

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

    Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

    Published on: July 5, 2024

    735
    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

    3.3K
    Lesion Explorer: A Video-guided, Standardized Protocol for Accurate and Reliable MRI-derived Volumetrics in Alzheimer's Disease and Normal Elderly
    12:50

    Lesion Explorer: A Video-guided, Standardized Protocol for Accurate and Reliable MRI-derived Volumetrics in Alzheimer's Disease and Normal Elderly

    Published on: April 14, 2014

    40.8K

    Area of Science:

    • Medical Imaging
    • Computer Vision
    • Ophthalmology

    Background:

    • Accurate segmentation of Optical Coherence Tomography (OCT) images is vital for diagnosing and monitoring retinal diseases.
    • Supervised learning for OCT segmentation is hindered by the laborious pixel-level annotation process.
    • Weakly Supervised Semantic Segmentation (WSSS) offers a solution by reducing annotation burden using weaker supervision like image-level labels.

    Purpose of the Study:

    • To develop a novel WSSS framework for OCT lesion segmentation using only image-level labels.
    • To integrate structural and text-driven guidance for generating high-quality pixel-level pseudo-labels.
    • To improve lesion localization and segmentation performance in OCT images.

    Main Methods:

    • Proposed a WSSS framework integrating structural and text-driven guidance for OCT lesion segmentation.
    • Employed two visual processing modules: one for original OCT images and another for layer segmentations with anomalous signals.
    • Leveraged large-scale pretrained models for label-derived and domain-agnostic synthetic textual guidance.
    • Fused visual and textual features in a multi-modal framework to align semantic meaning with structural relevance.

    Main Results:

    • Achieved state-of-the-art results on three OCT datasets.
    • Demonstrated improved lesion localization and segmentation performance.
    • Successfully generated high-quality, pixel-level pseudo-labels using weak supervision.

    Conclusions:

    • The proposed multi-modal WSSS framework effectively utilizes structural and text-driven guidance for OCT lesion segmentation.
    • This approach has the potential to significantly advance diagnostic accuracy and efficiency in medical imaging.
    • Publicly available code and models facilitate further research and application.