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Related Experiment Video

Updated: Mar 2, 2026

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Automated algorithm for counting microbleeds in patients with familial cerebral cavernous malformations.

Xiaowei Zou1, Blaine L Hart2, Marc Mabray2

  • 1Department of Radiology and Biomedical Imaging, University of California, San Francisco, San Francisco, California, USA.

Neuroradiology
|May 24, 2017
PubMed
Summary

An automated algorithm accurately counts cerebral cavernous malformation (CCM) microbleeds on SWI images, improving lesion quantification for familial CCM patients. This method offers a consistent approach for tracking disease progression.

Keywords:
Automated lesion countingCerebral cavernous malformationsMicrobleedsSusceptibility-weighted imaging

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Area of Science:

  • Neuroimaging
  • Medical Algorithms
  • Genetics

Background:

  • Familial cerebral cavernous malformation (CCM) is characterized by multiple, growing lesions.
  • Susceptibility-weighted imaging (SWI) reliably detects these lesions.
  • Manual microbleed counting is time-consuming and prone to variability.

Purpose of the Study:

  • To develop and validate an automated algorithm for counting cerebral cavernous malformation (CCM) microbleeds on SWI images.
  • To improve the accuracy and consistency of lesion quantification in familial CCM.

Main Methods:

  • Modified an existing algorithm for microbleed detection on SWI.
  • Trained the algorithm on manually delineated CCM microbleeds.
  • Compared automated counts with manual counts using linear regression and ICC.
  • Validated the algorithm's sensitivity against a consensus read.

Main Results:

  • Strong agreement between automated and manual counts (ICC=0.95 baseline, ICC=0.88 longitudinal).
  • No significant difference in average counts between manual and automated methods.
  • Algorithm achieved 50% sensitivity in validation, with strong agreement (ICC=0.77).

Conclusions:

  • The automated algorithm provides a consistent and reliable method for counting microbleeds in familial CCM.
  • This tool can significantly facilitate lesion quantification and tracking in CCM patients.
  • Automated counting aids in monitoring disease progression and treatment response.