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Neuroradiology

, Volume 59, Issue 7, pp 685–690 | Cite as

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

  • Xiaowei Zou
  • Blaine L. Hart
  • Marc Mabray
  • Mary R. Bartlett
  • Wei Bian
  • Jeffrey Nelson
  • Leslie A. Morrison
  • Charles E. McCulloch
  • Christopher P. Hess
  • Janine M. Lupo
  • Helen Kim
Functional Neuroradiology

Abstract

Purpose

Familial cerebral cavernous malformation (CCM) patients present with multiple lesions that can grow both in number and size over time and are reliably detected on susceptibility-weighted imaging (SWI). Manual counting of lesions is arduous and subject to high variability. We aimed to develop an automated algorithm for counting CCM microbleeds (lesions <5 mm in diameter) on SWI images.

Methods

Fifty-seven familial CCM type-1 patients were included in this institutional review board-approved study. Baseline SWI (n = 57) and follow-up SWI (n = 17) were performed on a 3T Siemens MR scanner with lesions counted manually by the study neuroradiologist. We modified an algorithm for detecting radiation-induced microbleeds on SWI images in brain tumor patients, using a training set of 22 manually delineated CCM microbleeds from two random scans. Manual and automated counts were compared using linear regression with robust standard errors, intra-class correlation (ICC), and paired t tests. A validation analysis comparing the automated counting algorithm and a consensus read from two neuroradiologists was used to calculate sensitivity, the proportion of microbleeds correctly identified by the automated algorithm.

Results

Automated and manual microbleed counts were in strong agreement in both baseline (ICC = 0.95, p < 0.001) and longitudinal (ICC = 0.88, p < 0.001) analyses, with no significant difference between average counts (baseline p = 0.11, longitudinal p = 0.29). In the validation analysis, the algorithm correctly identified 662 of 1325 microbleeds (sensitivity=50%), again with strong agreement between approaches (ICC = 0.77, p < 0.001).

Conclusion

The automated algorithm is a consistent method for counting microbleeds in familial CCM patients that can facilitate lesion quantification and tracking.

Keywords

Automated lesion counting Cerebral cavernous malformations Microbleeds Susceptibility-weighted imaging 

Notes

Acknowledgments

We would like to thank Dr. Sarah J. Nelson for her valuable advice on study development.

Compliance with ethical standards

Funding

This study was funded by a Brain Vascular Malformation Consortium (BVMC) postdoctoral training fellowship to XZ as part of National Institutes of Health (NIH) grant U54 NS065705. The BVMC is a part of the NIH Rare Diseases Clinical Research Network (RDCRN), an initiative of the Office of Rare Diseases Research (ORDR) and the National Center for Advancing Translational Science (NCATS). The consortium is funded through collaboration between NCATS and the National Institute of Neurological Disorders and Stroke (NINDS). The scientific computing resources in this study were provided by the Department of Radiology, University of California, San Francisco, and supported by NIH P01 CA118816.

Conflict of interest

The authors declare that they have no conflict of interest.

Ethical approval

All procedures performed in the studies involving human participants were in accordance with the ethical standards of the institutional review board of the University of New Mexico and with the 1964 Helsinki Declaration and its later amendments or comparable ethical standards.

Informed consent

Informed consent was obtained from all individual participants included in the study.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2017

Authors and Affiliations

  • Xiaowei Zou
    • 1
  • Blaine L. Hart
    • 2
  • Marc Mabray
    • 2
  • Mary R. Bartlett
    • 3
  • Wei Bian
    • 4
  • Jeffrey Nelson
    • 5
  • Leslie A. Morrison
    • 3
  • Charles E. McCulloch
    • 6
  • Christopher P. Hess
    • 1
  • Janine M. Lupo
    • 1
  • Helen Kim
    • 5
    • 6
  1. 1.Department of Radiology and Biomedical ImagingUniversity of California, San FranciscoSan FranciscoUSA
  2. 2.Department of RadiologyUniversity of New MexicoAlbuquerqueUSA
  3. 3.Department of NeurologyUniversity of New MexicoAlbuquerqueUSA
  4. 4.Department of RadiologyStanford UniversityStanfordUSA
  5. 5.Department of Anesthesia and Perioperative CareUniversity of California, San FranciscoSan FranciscoUSA
  6. 6.Department of Epidemiology and BiostatisticsUniversity of CaliforniaSan FranciscoUSA

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