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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

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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.