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 Experiment Video

Updated: Dec 30, 2025

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

Enabling a Single Deep Learning Model for Accurate Gland Instance Segmentation: A Shape-Aware Adversarial Learning

Zengqiang Yan, Xin Yang, Kwang-Ting Cheng

    IEEE Transactions on Medical Imaging
    |January 17, 2020
    PubMed
    Summary
    This summary is machine-generated.

    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

    Efficient and accurate neural-field reconstruction using resistive memory.

    Nature·2026
    Same author

    Resistive memory-based neural differential equation solver for score-based diffusion model.

    Nature communications·2026
    Same author

    Pruning random resistive memory for optimizing analog AI.

    Nature communications·2026
    Same author

    FedHAC: Towards Robust Federated Multi-Lesion Segmentation With Heterogeneous Annotation Completeness.

    IEEE journal of biomedical and health informatics·2025
    Same author

    A generalizable pathology foundation model using a unified knowledge distillation pretraining framework.

    Nature biomedical engineering·2025
    Same author

    Continuous-time digital twin with analog memristive neural ordinary differential equation solver.

    Science advances·2025
    Same journal

    LLM-enhanced Neuron Segmentation and Reconstruction in Complex Mouse Brain Images.

    IEEE transactions on medical imaging·2026
    Same journal

    Matrixed-Spectrum Decomposition Accelerated Linear Boltzmann Transport Equation Solver for Fast Scatter Correction in Multi-Spectral CT.

    IEEE transactions on medical imaging·2026
    Same journal

    The Ritz Adjoint Method for MRI Pulse Design.

    IEEE transactions on medical imaging·2026
    Same journal

    Physiology-guided Self-supervised Learning for Simultaneous Dual-Tracer PET Separation.

    IEEE transactions on medical imaging·2026
    Same journal

    Informed-Exploration Reinforcement Learning for Automated Virtual Coronary Intervention Planning.

    IEEE transactions on medical imaging·2026
    Same journal

    4D Reconstruction of Fetal Left Ventricle from Echocardiography via 2.5D Radial Segmentation and Graph-Fourier Reconstruction.

    IEEE transactions on medical imaging·2026
    See all related articles

    This study introduces a novel single deep learning model for accurate gland instance segmentation in histology images. The method uses a segment-level shape similarity measure to overcome boundary uncertainty, achieving state-of-the-art results.

    Area of Science:

    • Digital pathology
    • Medical image analysis
    • Computer vision

    Background:

    • Histology image analysis requires precise gland instance segmentation.
    • Boundary uncertainty in manual annotations complicates traditional pixel-based segmentation methods.
    • Existing multi-model approaches increase complexity and training challenges.

    Purpose of the Study:

    • To develop a single deep learning model for accurate gland instance segmentation.
    • To address the boundary uncertainty issue in histology image segmentation.
    • To improve efficiency and reduce complexity in gland segmentation models.

    Main Methods:

    • Proposed a segment-level shape similarity measure to handle boundary uncertainty, allowing for variations in boundary segment matching.

    More Related Videos

    Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications
    03:31

    Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

    Published on: December 15, 2023

    952

    Related Experiment Videos

    Last Updated: Dec 30, 2025

    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
    Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications
    03:31

    Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

    Published on: December 15, 2023

    952
  • Integrated multi-scale image data for global and local contextual information.
  • Introduced a pseudo domain adaptation framework for feature alignment to reduce training image variations.
  • Developed a shape-aware adversarial learning framework using segment-level similarity and adversarial loss.

    • Main Results:

    • Achieved state-of-the-art performance on the 2015 MICCAI Gland Challenge dataset using a single deep learning model.
    • Demonstrated improved accuracy and robustness in gland instance segmentation compared to existing methods.
    • The proposed method effectively handles boundary uncertainty and segmenting glands of various sizes.

    Conclusions:

    • The proposed shape-aware adversarial learning framework enables accurate gland instance segmentation with a single model.
    • The segment-level shape similarity measure is effective in addressing boundary uncertainty in medical image segmentation.
    • The approach shows broad applicability to other medical image segmentation tasks facing similar challenges.