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.
journal_name
J Digit Imagingjournal_title
Journal of digital imagingauthors
Chen Y,Villanueva-Meyer JE,Morrison MA,Lupo JMdoi
10.1007/s10278-018-0146-zsubject
Has Abstractpub_date
2019-10-01 00:00:00pages
766-772issue
5eissn
0897-1889issn
1618-727Xpii
10.1007/s10278-018-0146-zjournal_volume
32pub_type
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