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

  • Yicheng Chen
  • Javier E. Villanueva-Meyer
  • Melanie A. Morrison
  • Janine M. LupoEmail author
Article

Abstract

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.

Keywords

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

Notes

Funding Information

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.

Compliance with Ethical Standards

Conflict of Interest

The authors declare that they have no conflict of interest with regard to the content of this manuscript.

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Copyright information

© Society for Imaging Informatics in Medicine 2018
corrected publication 2018

Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.

Authors and Affiliations

  1. 1.UCSF-UC Berkeley Graduate Program in BioengineeringSan FranciscoUSA
  2. 2.Department of Radiology and Biomedical ImagingUniversity of California San FranciscoSan FranciscoUSA

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