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Structure-aware multi-task learning with domain generalization for robust vertebrae analysis in spinal CT.

Jianyang Du1,2,3, Heng'an Ge4, Rui Zhang5

  • 1Cancer Center, Department of Neurosurgery, Zhejiang Provincial People's Hospital, Affiliated People's Hospital, Hangzhou Medical College, Hangzhou, Zhejiang, China.

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Summary

VertebraFormer enhances spinal CT analysis by unifying segmentation, identification, and lesion localization. This robust framework demonstrates improved accuracy and generalizability across diverse clinical imaging datasets.

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Area of Science:

  • Medical image analysis
  • Artificial intelligence in healthcare
  • Spinal imaging

Background:

  • Spinal image analysis is crucial for diagnosing musculoskeletal and neurological disorders.
  • Current vertebrae segmentation methods lack generalizability and do not address downstream tasks like identification and lesion localization.
  • A need exists for robust and adaptable spinal CT analysis frameworks.

Purpose of the Study:

  • To introduce VertebraFormer, a unified multi-task framework for robust and generalizable spinal CT analysis.
  • To develop a method addressing vertebrae segmentation, identification, and lesion localization simultaneously.
  • To create a heterogeneous benchmark dataset (MultiSpine) for evaluating spinal CT analysis methods.

Main Methods:

  • Developed VertebraFormer, a Transformer-based framework with task-specific decoders and a dynamic modulation unit.
  • Curated MultiSpine, a benchmark dataset with diverse CT volumes and annotations for segmentation, labeling, and pathology.
  • Evaluated the framework on vertebrae segmentation, numbering, and lesion localization tasks in-domain and cross-domain settings.

Main Results:

  • VertebraFormer significantly outperformed existing methods in accuracy and robustness across multiple tasks.
  • The framework demonstrated strong generalizability across different clinical imaging domains.
  • Ablation, perturbation, and efficiency analyses confirmed the framework's validity and performance.

Conclusions:

  • VertebraFormer offers a unified and robust solution for comprehensive spinal CT analysis.
  • The framework shows potential for improving diagnostic accuracy and treatment planning in spinal disorders.
  • The MultiSpine benchmark facilitates further research in generalizable spinal image analysis.