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: Oct 25, 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

3.1K

HD-RDS-UNet: Leveraging Spatial-Temporal Correlation Between the Decoder Feature Maps for Lymphoma Segmentation.

Meng Wang, Huiyan Jiang, Tianyu Shi

    IEEE Journal of Biomedical and Health Informatics
    |August 5, 2021
    PubMed
    Summary

    This study introduces the HD-RDS-UNet for lymphoma segmentation in PET/CT scans, achieving high accuracy and efficiency. The novel architecture effectively uses spatial and temporal correlations for improved medical image analysis.

    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

    PCSK9 inhibitor failure in a statin-intolerant FH patient with a novel LDLR variant: a case report.

    Frontiers in cardiovascular medicine·2025
    Same author

    Recent Advancements in Hyperspectral Image Reconstruction from a Compressive Measurement.

    Sensors (Basel, Switzerland)·2025
    Same author

    Multi-Task Collaborative Assisted Training Method for Grouping Fuzzy Categories Classification of Cervical Cancer Cells.

    IEEE journal of biomedical and health informatics·2025
    Same author

    NpCIPK6-NpSnRK1 module facilitates intersubgeneric hybridization barriers in water lily (<i>Nymphaea</i>) by reducing abscisic acid content.

    Horticulture research·2025
    Same author

    Cells Grouping Detection and Confusing Labels Correction on Cervical Pathology Images.

    Bioengineering (Basel, Switzerland)·2025
    Same author

    Label credibility correction based on cell morphological differences for cervical cells classification.

    Scientific reports·2025

    Area of Science:

    • Medical Imaging
    • Artificial Intelligence
    • Oncology

    Background:

    • Accurate lymphoma segmentation in medical images is crucial but challenging.
    • UNet architectures excel at spatial correlations but miss temporal ones.
    • Integrating Recurrent Neural Networks (RNNs) with UNet is complex.

    Purpose of the Study:

    • To propose a novel UNet-based architecture for improved lymphoma segmentation.
    • To effectively capture both spatial and temporal correlations in medical image data.
    • To enhance the efficiency and performance of automated medical image segmentation.

    Main Methods:

    • Developed a Hyper Dense Encoder and Recurrent Dense Siamese Decoder (HD-RDS-UNet).
    • Utilized a weighted Dice loss function for stable training with self-adaptive parameters.

    More Related Videos

    Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma
    04:09

    Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma

    Published on: October 10, 2018

    8.4K

    Related Experiment Videos

    Last Updated: Oct 25, 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

    3.1K
    Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma
    04:09

    Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma

    Published on: October 10, 2018

    8.4K
  • Conducted patient-independent five-fold cross-validation on 3D whole-body PET/CT lymphoma scans.

    • Main Results:

    • Achieved a volume-wise average Dice score of 85.58% and sensitivity of 94.63%.
    • Attained a patient-wise average Dice score of 85.85% and sensitivity of 95.01%.
    • Demonstrated consistent superiority across different HD-RDS-UNet configurations and efficient pruning capabilities.

    Conclusions:

    • The HD-RDS-UNet model significantly improves lymphoma segmentation accuracy and efficiency.
    • The proposed architecture effectively leverages spatial-temporal correlations for medical image analysis.
    • HD-RDS-UNet offers a promising solution for automated segmentation in clinical oncology settings.