Toward Automatic Detection of Radiation-Induced Cerebral Microbleeds Using a 3D Deep Residual Network


Cerebral microbleeds, which are small focal hemorrhages in the brain that are prevalent in many diseases, are gaining increasing attention due to their potential as surrogate markers of disease burden, clinical outcomes, and delayed effects of therapy. Manual detection is laborious and automatic detection and labeling of these lesions is challenging using traditional algorithms. Inspired by recent successes of deep convolutional neural networks in computer vision, we developed a 3D deep residual network that can distinguish true microbleeds from false positive mimics of a previously developed technique based on traditional algorithms. A dataset of 73 patients with radiation-induced cerebral microbleeds scanned at 7 T with susceptibility-weighted imaging was used to train and evaluate our model. With the resulting network, we maintained 95% of the true microbleeds in 12 test patients and the average number of false positives was reduced by 89%, achieving a detection precision of 71.9%, higher than existing published methods. The likelihood score predicted by the network was also evaluated by comparing to a neuroradiologist’s rating, and good correlation was observed.

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  • 08 February 2019

    This paper was published inadvertently as open access. It has been corrected online.


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This work was supported by the National Institute for Child Health and Human Development of the National Institutes of Health grant R01HD079568 and GE Healthcare.

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Correspondence to Janine M. Lupo.

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The original version of this article was revised: with the author's decision to step back from Open Choice, the copyright of the article changed to © Society for Imaging Informatics in Medicine 2018 and the article is forthwith distributed under the terms of copyright.

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Chen, Y., Villanueva-Meyer, J.E., Morrison, M.A. et al. Toward Automatic Detection of Radiation-Induced Cerebral Microbleeds Using a 3D Deep Residual Network. J Digit Imaging 32, 766–772 (2019).

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  • Deep learning
  • Susceptibility-weighted imaging
  • Cerebral microbleeds
  • Convolutional neural networks
  • Automated-detection