Multimodal predictive modeling of endovascular treatment outcome for acute ischemic stroke using machine-learning
G Brugnara, U Neuberger, MA Mahmutoglu, M Foltyn… - Stroke, 2020 - Am Heart Assoc
Background and Purpose: This study assessed the predictive performance and relative
importance of clinical, multimodal imaging, and angiographic characteristics for predicting …
importance of clinical, multimodal imaging, and angiographic characteristics for predicting …
Machine learning–enabled automated determination of acute ischemic core from computed tomography angiography
Background and Purpose—The availability of and expertise to interpret advanced
neuroimaging recommended in the guideline-based endovascular stroke therapy (EST) …
neuroimaging recommended in the guideline-based endovascular stroke therapy (EST) …
Intracranial volume measurement: a systematic review and comparison of different techniques
The ability to calculate intracranial volume (ICV) from 3-dimensional imaging is a useful tool
in a craniofacial team's armamentarium. Intracranial volume uses range from decision …
in a craniofacial team's armamentarium. Intracranial volume uses range from decision …
3D deep neural network segmentation of intracerebral hemorrhage: development and validation for clinical trials
Intracranial hemorrhage (ICH) occurs when a blood vessel ruptures in the brain. This leads
to significant morbidity and mortality, the likelihood of which is predicated on the size of the …
to significant morbidity and mortality, the likelihood of which is predicated on the size of the …
State-of-the-art traditional to the machine-and deep-learning-based skull stripping techniques, models, and algorithms
Several neuroimaging processing applications consider skull stripping as a crucial pre-
processing step. Due to complex anatomical brain structure and intensity variations in brain …
processing step. Due to complex anatomical brain structure and intensity variations in brain …
Recommendations for processing head CT data
J Muschelli - Frontiers in neuroinformatics, 2019 - frontiersin.org
Many research applications of neuroimaging use magnetic resonance imaging (MRI). As
such, recommendations for image analysis and standardized imaging pipelines exist …
such, recommendations for image analysis and standardized imaging pipelines exist …
[HTML][HTML] PItcHPERFeCT: primary intracranial hemorrhage probability estimation using random forests on CT
Introduction Intracerebral hemorrhage (ICH), where a blood vessel ruptures into areas of the
brain, accounts for approximately 10–15% of all strokes. X-ray computed tomography (CT) …
brain, accounts for approximately 10–15% of all strokes. X-ray computed tomography (CT) …
Transformer-based out-of-distribution detection for clinically safe segmentation
In a clinical setting it is essential that deployed image processing systems are robust to the
full range of inputs they might encounter and, in particular, do not make confidently wrong …
full range of inputs they might encounter and, in particular, do not make confidently wrong …
Head CT: toward making full use of the information the X-rays give
KA Cauley, Y Hu, SW Fielden - American Journal of …, 2021 - Am Soc Neuroradiology
Although clinical head CT images are typically interpreted qualitatively, automated methods
applied to routine clinical head CTs enable quantitative assessment of brain volume, brain …
applied to routine clinical head CTs enable quantitative assessment of brain volume, brain …
Determining ischemic stroke from CT-angiography imaging using symmetry-sensitive convolutional networks
Acute Ischemic Stroke (AIS) is the second leading cause of death worldwide in 2015, and 5
th in the United States. Neuro-imaging is routinely used in the diagnosis and management of …
th in the United States. Neuro-imaging is routinely used in the diagnosis and management of …