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Updated: May 23, 2026

Automated Joint Space Detection Improves Bone Segmentation Accuracy
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Published on: November 28, 2025

Anatomy-Guided Spatiotemporal Affinity Learning for Unsupervised Domain Adaptation in Echocardiography Segmentation.

Xinyan Fang, Xinghua Ma, Yang Liu

    IEEE Journal of Biomedical and Health Informatics
    |May 21, 2026
    PubMed
    Summary
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    Unsupervised domain adaptation for left ventricle segmentation in echocardiography is improved by an anatomy-guided framework. This method aligns anatomical context and refines features, enhancing segmentation accuracy across diverse datasets.

    Area of Science:

    • Medical Imaging
    • Artificial Intelligence
    • Cardiovascular Imaging

    Background:

    • Unsupervised domain adaptation (UDA) is crucial for left ventricle (LV) segmentation in echocardiography to ensure clinical utility across various devices and institutions.
    • Challenges in UDA for echocardiography include anatomical context shifts and noise interference, hindering robust segmentation performance.
    • Existing UDA methods struggle to address these domain-specific challenges effectively.

    Purpose of the Study:

    • To introduce a novel anatomy-guided spatio-temporal affinity framework for unsupervised domain adaptation in left ventricle segmentation.
    • To address anatomical context shift and noise interference in echocardiographic images.
    • To improve the generalizability and clinical applicability of LV segmentation models.

    Main Methods:

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    Last Updated: May 23, 2026

    Automated Joint Space Detection Improves Bone Segmentation Accuracy
    06:45

    Automated Joint Space Detection Improves Bone Segmentation Accuracy

    Published on: November 28, 2025

    • Developed an anatomy-guided spatio-temporal affinity framework comprising Anatomical Context Alignment (ACA) and Anatomical Affinity Refinement (AAR) modules.
    • ACA module adapts source domain to target domain's anatomical context using LV-dominant cropping and 4C-complete mirroring.
    • AAR module enforces fine-grained anatomical consistency and noise suppression via spatial (SAR) and temporal (TAR) affinity modeling.

    Main Results:

    • Demonstrated that anatomical context shift is a primary driver of domain discrepancy in echocardiographic LV segmentation.
    • The proposed framework effectively alleviates anatomical context shift and significantly suppresses noise interference.
    • Achieved superior performance compared to state-of-the-art UDA methods on three public datasets (CAMUS, EchoNet-Dynamic, CardiacUDA).

    Conclusions:

    • The anatomy-guided spatio-temporal affinity framework successfully enhances unsupervised domain adaptation for left ventricle segmentation.
    • The method's ability to align anatomical context and refine features leads to improved segmentation accuracy and robustness.
    • This work offers a promising solution for deploying reliable echocardiographic LV segmentation in diverse clinical settings.