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Updated: May 31, 2026

Automated Joint Space Detection Improves Bone Segmentation Accuracy
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VerTE-MT: A Multi-Task Framework with Entropy-Guided Sampling for Vertebrae Segmentation and Localisation in CT.

Sevde Aydogdu, Razvan Caramalau, Abir Dutta

    IEEE Journal of Biomedical and Health Informatics
    |May 29, 2026
    PubMed
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    This study introduces VerTE-MT, a single-stage, multi-task learning framework for automated spinal CT analysis. It achieves high accuracy in vertebrae segmentation and localization, even for challenging pathological cases.

    Area of Science:

    • Medical Imaging
    • Computer Vision
    • Artificial Intelligence

    Background:

    • Automated spinal CT analysis requires accurate vertebrae segmentation and localization.
    • Existing methods often use multi-stage pipelines, neglecting the inherent relationship between segmentation and localization.
    • Pathological variations and anatomical similarities pose significant challenges to current approaches.

    Purpose of the Study:

    • To develop a novel single-stage, multi-task learning framework (VerTE-MT) for concurrent vertebrae segmentation and centroid localization.
    • To improve the accuracy and robustness of automated spinal CT analysis, particularly for challenging cases.

    Main Methods:

    • Proposed VerTE-MT framework with a shared volumetric encoder, Vision Transformer bottleneck, and dual decoders.

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  • Integrated entropy-guided sampling to prioritize under-represented vertebrae.
  • Concurrent segmentation and localization tasks within a single network.

    • Main Results:

    • Achieved high Dice scores for vertebral column segmentation (84.18%-85.45%) and L6 segmentation (81.03%-75.96%) on the VerSe'20 dataset.
    • Reduced segmentation boundary errors, with Hausdorff Distance decreasing by up to 4.62mm.
    • Maintained robust localization with mean error below 10mm.
    • Demonstrated zero-shot validation performance on scoliotic datasets (mean Dice 83.03% and 65.17%).

    Conclusions:

    • VerTE-MT offers a superior single-stage approach for vertebrae segmentation and localization compared to existing methods.
    • The framework shows significant potential for handling pathological spinal anatomies and unseen cases.
    • This approach enhances the efficiency and accuracy of automated spinal CT analysis.