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Related Experiment Video

Updated: May 9, 2025

Automated Midline Shift and Intracranial Pressure Estimation based on Brain CT Images
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Segmentation-assisted vessel centerline extraction from cerebral CT Angiography.

Sijie Liu1,2,3, Ruisheng Su2,4, Jianghang Su2

  • 1Institute of Applied Electronics, China Academy of Engineering Physics, Mianyang, China.

Medical Physics
|April 28, 2025
PubMed
Summary

This study introduces a novel framework for automated brain vessel centerline extraction from CT angiography (CTA) images, improving accuracy and efficiency for stroke diagnosis. The DTUNet model enhances vessel analysis without requiring extra physician annotation.

Keywords:
braincerebrovascular disordersdeep learningstrokex‐ray computed tomography

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Area of Science:

  • Medical Imaging
  • Neuroscience
  • Computer Vision

Background:

  • Accurate extraction of brain vessel centerlines from CT angiography (CTA) is crucial for diagnosing and treating cerebrovascular diseases like stroke.
  • Challenges include complex cerebrovascular structures and variable imaging quality in CTA scans.

Purpose of the Study:

  • To develop and validate a segmentation-assisted framework for enhanced accuracy and efficiency in brain vessel centerline extraction from CTA images.
  • To streamline lumen segmentation generation without additional physician annotation, thereby improving centerline extraction effectiveness.

Main Methods:

  • A framework integrating pre-processing (image registration, patch division), lumen segmentation generation (graph cuts, kernel regression), a dual-branch topology-aware UNet (DTUNet) with topology-aware loss (TAL), and post-processing (skeletonization, refinement).
  • The DTUNet optimizes the use of annotated centerlines and generated lumen segmentation through its dual-branch structure and TAL.

Main Results:

  • The framework was evaluated on an in-house dataset of 10 intracranial and 40 sub-image CTA scans.
  • Demonstrated superior performance over state-of-the-art methods in average symmetric centerline distance (ASCD) and overlap (OV).
  • Achieved ASCD of 0.84 and OV of 0.885 for intracranial images, and ASCD of 1.26 and OV of 0.824 for sub-images, showing promise for clinical stroke applications.

Conclusions:

  • The DTUNet framework automates lumen segmentation and optimizes network design for high-performance vessel centerline extraction.
  • Achieves excellent results without additional annotation demands, offering significant benefits for cerebrovascular disease management.