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Updated: May 12, 2026

Manual Segmentation of the Human Choroid Plexus Using Brain MRI
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Development of image segmentation methods for intracranial aneurysms.

Yuka Sen1, Yi Qian, Alberto Avolio

  • 1The Australian School of Advanced Medicine, Macquarie University, Sydney, NSW 2109, Australia.

Computational and Mathematical Methods in Medicine
|April 23, 2013
PubMed
Summary

This study introduces a new Threshold-Based Level Set (TLS) method for vascular segmentation, significantly improving accuracy for aneurysm analysis. The TLS method reduces volume differences to approximately 5%, overcoming limitations of existing techniques for complex cerebrovascular shapes.

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Area of Science:

  • Medical Imaging
  • Computational Anatomy
  • Biomedical Engineering

Background:

  • Vascular segmentation is crucial for diagnosing and treating vascular pathologies but faces significant challenges.
  • Existing methods like Region Growing Threshold and Chan-Vese models have limitations in accuracy and handling complex anatomy.

Purpose of the Study:

  • To validate existing vascular segmentation methods for aneurysm analysis.
  • To propose and evaluate a novel Threshold-Based Level Set (TLS) method to overcome current segmentation challenges.

Main Methods:

  • Validation of two existing segmentation methods (Region Growing Threshold, Chan-Vese) against manual segmentation for eight aneurysms.
  • Development and application of a new Threshold-Based Level Set (TLS) method.
  • Comparative analysis of volume differences and influence of anatomical shape on segmentation results.

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Last Updated: May 12, 2026

Manual Segmentation of the Human Choroid Plexus Using Brain MRI
04:25

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Published on: December 15, 2023

Automated Midline Shift and Intracranial Pressure Estimation based on Brain CT Images
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Published on: April 13, 2013

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Main Results:

  • Existing methods showed maximum volume differences of up to 24% and were significantly influenced by aneurysm shape.
  • The proposed TLS method achieved a significantly lower volume difference of approximately 5%.
  • The TLS method demonstrated potential for automatic segmentation without requiring seed points or intensity thresholds.

Conclusions:

  • The TLS method offers a more accurate and efficient approach to cerebrovascular segmentation, particularly for anatomically complex shapes.
  • This technique has the potential to enhance medical imagery simulations for better decision-making in vascular pathology treatment.