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Deep-learned 3D black-blood imaging using automatic labelling technique and 3D convolutional neural networks for detecting metastatic brain tumors

Title
Deep-learned 3D black-blood imaging using automatic labelling technique and 3D convolutional neural networks for detecting metastatic brain tumors
Authors
Jun Y.Eo T.Kim T.Shin H.Hwang D.Bae S.H.Park Y.W.Lee H.-J.Choi B.W.Ahn S.S.
Ewha Authors
박예원
Issue Date
2018
Journal Title
Scientific Reports
ISSN
2045-2322JCR Link
Citation
Scientific Reports vol. 8, no. 1
Publisher
Nature Publishing Group
Indexed
SCIE; SCOPUS WOS scopus
Document Type
Article
Abstract
Black-blood (BB) imaging is used to complement contrast-enhanced 3D gradient-echo (CE 3D-GRE) imaging for detecting brain metastases, requiring additional scan time. In this study, we proposed deep-learned 3D BB imaging with an auto-labelling technique and 3D convolutional neural networks for brain metastases detection without additional BB scan. Patients were randomly selected for training (29 sets) and testing (36 sets). Two neuroradiologists independently evaluated deep-learned and original BB images, assessing the degree of blood vessel suppression and lesion conspicuity. Vessel signals were effectively suppressed in all patients. The figure of merits, which indicate the diagnostic performance of radiologists, were 0.9708 with deep-learned BB and 0.9437 with original BB imaging, suggesting that the deep-learned BB imaging is highly comparable to the original BB imaging (difference was not significant; p = 0.2142). In per patient analysis, sensitivities were 100% for both deep-learned and original BB imaging; however, the original BB imaging indicated false positive results for two patients. In per lesion analysis, sensitivities were 90.3% for deep-learned and 100% for original BB images. There were eight false positive lesions on the original BB imaging but only one on the deep-learned BB imaging. Deep-learned 3D BB imaging can be effective for brain metastases detection. © 2018 The Author(s).
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DOI
10.1038/s41598-018-27742-1
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의료원 > 의료원 > Journal papers
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