Intracerebral Hemorrhage Segmentation on Noncontrast Computed Tomography Using a Masked Loss Function U-Net Approach-Reference-Cited by-同舟云学术

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Intracerebral Hemorrhage Segmentation on Noncontrast Computed Tomography Using a Masked Loss Function U-Net Approach

Published:2022-10-11 Issue:1 Volume:47 Page:93-101
ISSN:1532-3145
Container-title:Journal of Computer Assisted Tomography
language:en
Short-container-title:J Comput Assist Tomogr

Author:

Coorens Nadine A.,Lipman Kevin Groot,Krishnam Sanjith P.,Tan Can Ozan,Alic Lejla,Gupta Rajiv

Publisher

Ovid Technologies (Wolters Kluwer Health)

Subject

Radiology, Nuclear Medicine and imaging

Reference29 articles.

1. Worldwide stroke incidence and early case fatality reported in 56 population-based studies: a systematic review;Lancet Neurol,2009

2. International epidemiology of intracerebral hemorrhage;Curr Atheroscler Rep,2012

3. Incidence, case fatality, and functional outcome of intracerebral haemorrhage over time, according to age, sex, and ethnic origin: a systematic review and meta-analysis;Lancet Neurol,2010

4. Rate of contrast extravasation on computed tomographic angiography predicts hematoma expansion and mortality in primary intracerebral hemorrhage;Stroke,2015

5. Epidemiology, risk factors, and clinical features of intracerebral hemorrhage: an update;J Stroke,2017

Cited by 2 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Do Deep Learning Algorithms Accurately Segment Intracerebral Hemorrhages on Noncontrast Computed Tomography? A Systematic Review and Meta‐Analysis;Stroke: Vascular and Interventional Neurology;2024-07

2. Deep learning-assisted detection and segmentation of intracranial hemorrhage in noncontrast computed tomography scans of acute stroke patients: a systematic review and meta-analysis;International Journal of Surgery;2024-03-15

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