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 det...
| Publicado en: | Journal of Digital Imaging Vol. 32; no. 5; pp. 766 - 773 |
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| Autores principales: | , , , |
| Formato: | diagnostic images research tables/charts Journal Article |
| Publicado: |
Springer Nature
Oct2019
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=138543051&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 138543051 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 08971889 DOQ jtl: Journal of Digital Imaging issn: 08971889 maglogo: N pubinfo: dt: Oct2019 vid: 32 iid: 5 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 138543051 138543051 143917356 138543051 10.1007/s10278-018-0146-z 138543051 ppf: 766 ppct: 7 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Toward Automatic Detection of Radiation-Induced Cerebral Microbleeds Using a 3D Deep Residual Network. aug: au: Chen, Yicheng Villanueva-Meyer, Javier E. Morrison, Melanie A. Lupo, Janine M. affil: UCSF-UC Berkeley Graduate Program in Bioengineering, San Francisco, USA sug: subj: Cerebral Hemorrhage Etiology Radiation Injuries Cerebral Hemorrhage Diagnosis Imaging, Three-Dimensional Image Interpretation, Computer Assisted Methods Algorithms Human Minimum Data Set Magnetic Resonance Imaging Methods Descriptive Statistics False Positive Results Precision Neuroradiography Radiologists Correlation Coefficient ab: 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. pubtype: Academic Journal doctype: diagnostic images research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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