AIMC Topic: Cerebral Hemorrhage

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Exploring linearity of deep neural network trained QSM: QSMnet.

NeuroImage Feb 7, 2020
Recently, deep neural network-powered quantitative susceptibility mapping (QSM), QSMnet, successfully performed ill-conditioned dipole inversion in QSM and generated high-quality susceptibility maps. In this paper, the network, which was trained by h...
Humans Magnetic Resonance Imaging Computer Simulation Image Interpretation, Computer-Assisted Cerebral Hemorrhage Artifacts Adult Neuroimaging Deep Learning
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Fidelity imposed network edit (FINE) for solving ill-posed image reconstruction.

NeuroImage Jan 22, 2020
Deep learning (DL) is increasingly used to solve ill-posed inverse problems in medical imaging, such as reconstruction from noisy and/or incomplete data, as DL offers advantages over conventional methods that rely on explicit image features and hand ...
Deep Learning Image Processing, Computer-Assisted Humans Magnetic Resonance Imaging Brain Multiple Sclerosis Cerebral Hemorrhage Computer Simulation Neuroimaging Adult
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Machine learning models for identifying preterm infants at risk of cerebral hemorrhage.

PloS one Jan 15, 2020
Intracerebral hemorrhage in preterm infants is a major cause of brain damage and cerebral palsy. The pathogenesis of cerebral hemorrhage is multifactorial. Among the risk factors are impaired cerebral autoregulation, infections, and coagulation disor...
Male Infant, Extremely Premature Cerebral Hemorrhage Models, Cardiovascular Retrospective Studies Female Infant, Newborn Risk Factors Humans Machine Learning
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Deep Learning for Automated Measurement of Hemorrhage and Perihematomal Edema in Supratentorial Intracerebral Hemorrhage.

Stroke Dec 6, 2019
Background and Purpose- Volumes of hemorrhage and perihematomal edema (PHE) are well-established biomarkers of primary and secondary injury, respectively, in spontaneous intracerebral hemorrhage. An automated imaging pipeline capable of accurately an...
Male Middle Aged Deep Learning Cohort Studies Humans Algorithms Female Cerebral Hemorrhage Brain Edema Hematoma Edema
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Artificial intelligence vs human intelligence: will radiologists be needed in the future?

Radiologia Dec 5, 2019
Radiology Radiologists Intelligence Artifacts Forecasting Tomography, X-Ray Computed Diagnosis, Computer-Assisted Cerebral Hemorrhage Neuroradiography Humans Artificial Intelligence
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Fully Automated Segmentation Algorithm for Hematoma Volumetric Analysis in Spontaneous Intracerebral Hemorrhage.

Stroke Nov 18, 2019
Background and Purpose- Hematoma volume measurements influence prognosis and treatment decisions in patients with spontaneous intracerebral hemorrhage (ICH). The aims of this study are to derive and validate a fully automated segmentation algorithm f...
Humans Deep Learning Cerebral Hemorrhage Hematoma Image Interpretation, Computer-Assisted Neuroimaging
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Radiomics features on non-contrast computed tomography predict early enlargement of spontaneous intracerebral hemorrhage.

Clinical neurology and neurosurgery Aug 15, 2019
OBJECTIVE: To explore the value of radiomics features on non-contrast computed tomography (NCCT) in predicting early enlargement of spontaneous intracerebral hemorrhage (SICH).
Image Processing, Computer-Assisted Support Vector Machine Cerebral Hemorrhage Hematoma Aged Male Middle Aged Disease Progression Humans Prognosis Tomography, X-Ray Computed Female Machine Learning Algorithms
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Cerebral microbleed detection using Susceptibility Weighted Imaging and deep learning.

NeuroImage May 20, 2019
Detecting cerebral microbleeds (CMBs) is important in diagnosing a variety of diseases including dementia, stroke and traumatic brain injury. However, manual detection of CMBs can be time-consuming and prone to errors, whereas the current automatic a...
Cerebral Hemorrhage Image Interpretation, Computer-Assisted Adolescent Humans Child, Preschool Algorithms Adult Child Brain Retrospective Studies Sensitivity and Specificity Magnetic Resonance Imaging Aged Middle Aged Aged, 80 and over Deep Learning Young Adult
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Prediction of hematoma expansion in spontaneous intracerebral hemorrhage using support vector machine.

EBioMedicine May 3, 2019
BACKGROUND: Spontaneous intracerebral hemorrhage (ICH) is a devastating disease with high mortality rate. This study aimed to predict hematoma expansion in spontaneous ICH from routinely available variables by using support vector machine (SVM) metho...
Prognosis Retrospective Studies Tomography, X-Ray Computed Female Aged Hematoma Aged, 80 and over Odds Ratio Image Processing, Computer-Assisted Young Adult Glasgow Coma Scale Support Vector Machine Adult Cerebral Hemorrhage Humans Sensitivity and Specificity Models, Biological Biomarkers Male Middle Aged ROC Curve
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Plaque components segmentation in carotid artery on simultaneous non-contrast angiography and intraplaque hemorrhage imaging using machine learning.

Magnetic resonance imaging Apr 5, 2019
PURPOSE: This study sought to determine the feasibility of using Simultaneous Non-contrast Angiography and intraPlaque Hemorrhage (SNAP) to detect the lipid-rich/necrotic core (LRNC), and develop a machine learning based algorithm to segment plaque c...
Plaque, Amyloid Middle Aged Reproducibility of Results Plaque, Atherosclerotic Cerebral Hemorrhage Contrast Media Aged Angiography Bayes Theorem Calcinosis Humans Sensitivity and Specificity Necrosis Carotid Arteries Lipids Male Machine Learning Carotid Artery, Common Carotid Stenosis Algorithms Female
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