2019
DOI: 10.1007/978-3-030-32248-9_91
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Radiologist-Level Stroke Classification on Non-contrast CT Scans with Deep U-Net

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Cited by 18 publications
(5 citation statements)
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“…Based on deep learning methods, with the development of deep learning in image segmentation [ 22 ], many studies have also applied it to the segmentation task of intracranial hemorrhage. Manvel et al [ 23 ] combined improved U-Net [ 24 ] and a dual-path network [ 25 ], which segmented the intracranial hemorrhage area from three perspectives and finally merged multiple separate training networks to predict the intracranial hemorrhage area. Islam et al [ 26 ] extract output feature maps of VGG network and combine them into supercolumn features for intracranial hemorrhage segmentation.…”
Section: Related Workmentioning
confidence: 99%
“…Based on deep learning methods, with the development of deep learning in image segmentation [ 22 ], many studies have also applied it to the segmentation task of intracranial hemorrhage. Manvel et al [ 23 ] combined improved U-Net [ 24 ] and a dual-path network [ 25 ], which segmented the intracranial hemorrhage area from three perspectives and finally merged multiple separate training networks to predict the intracranial hemorrhage area. Islam et al [ 26 ] extract output feature maps of VGG network and combine them into supercolumn features for intracranial hemorrhage segmentation.…”
Section: Related Workmentioning
confidence: 99%
“…Other studies that aimed to develop automated infarct segmentation on NCCT have used deep learning models trained on manual NCCT segmentations by neuroradiologists. 16–20 As shown in this study, there is substantial variation in manual segmentations even among experts. Similarly to algorithms trained on DWI, algorithms trained on manual segmentations will also consider other characteristics, such as tissue swelling or loss of differentiation between gray and white matter.…”
Section: Discussionmentioning
confidence: 70%
“…These can occur as a result of a rupture in the blood-brain barrier, extravasation (enhancement) of contrast medium, or hemorrhage. 22 Based on study findings, it may be preferable to use MRI in the acute evaluation of patients with TIAMS to identify those with the highest risk of recurrent stroke. Currently, the use of MRI for TIAMS evaluation is highly variable, and clinicians have limited ability to predict which patients will have infarct based on imaging.…”
Section: Discussionmentioning
confidence: 99%