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Related Experiment Video

Updated: May 22, 2026

Automated Joint Space Detection Improves Bone Segmentation Accuracy
06:45

Automated Joint Space Detection Improves Bone Segmentation Accuracy

Published on: November 28, 2025

Pose Guided Unsupervised Domain Adaptation for Human Body Part Segmentation.

Arindam Dutta, Rohit Lal, Yash Garg

    IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society
    |May 20, 2026
    PubMed
    Summary
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    We developed POSTURE, a novel pose-guided method for human body part segmentation. It significantly improves accuracy on new datasets by using anatomical pose information for domain adaptation.

    Area of Science:

    • Computer Vision
    • Machine Learning
    • Medical Image Analysis

    Background:

    • Current human body part segmentation algorithms struggle with domain shifts, leading to performance degradation.
    • End-to-end supervised methods are sensitive to variations in data distribution.

    Purpose of the Study:

    • To introduce POSTURE (Pose Guided Unsupervised Domain Adaptation for Human Body Part Segmentation), a method to enhance segmentation performance on unlabeled target data.
    • To address the limitations of existing domain adaptive methods by incorporating anatomical priors.

    Main Methods:

    • POSTURE utilizes a pseudo-labelling approach guided by anatomical pose keypoints.
    • It leverages the underlying structure of the human body to drive the domain adaptation process.
    • The method is extended to source-free settings (SF-POSTURE) for privacy and computational efficiency.

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

    Main Results:

    • POSTURE achieved an average improvement of 8% over state-of-the-art domain adaptive semantic segmentation methods.
    • Performance gains were demonstrated across three benchmark datasets.
    • SF-POSTURE maintained performance with negligible degradation in source-free scenarios.

    Conclusions:

    • POSTURE offers a robust solution for human body part segmentation under domain shifts.
    • Incorporating anatomical guidance provides a strong inductive prior for effective domain adaptation.
    • The source-free extension enhances the method's practicality and applicability.