Automated detection and screening of traumatic brain injury (TBI) using computed tomography images: a comprehensive review and future perspectives
Traumatic brain injury (TBI) occurs due to the disruption in the normal functioning of the
brain by sudden external forces. The primary and secondary injuries due to TBI include …
brain by sudden external forces. The primary and secondary injuries due to TBI include …
Computational approaches for acute traumatic brain injury image recognition
E Lin, EL Yuh - Frontiers in neurology, 2022 - frontiersin.org
In recent years, there have been major advances in deep learning algorithms for image
recognition in traumatic brain injury (TBI). Interest in this area has increased due to the …
recognition in traumatic brain injury (TBI). Interest in this area has increased due to the …
[HTML][HTML] A deep learning algorithm for automatic detection and classification of acute intracranial hemorrhages in head CT scans
Acute Intracranial hemorrhage (ICH) is a life-threatening disease that requires emergency
medical attention, which is routinely diagnosed using non-contrast head CT imaging. The …
medical attention, which is routinely diagnosed using non-contrast head CT imaging. The …
Intracranial hemorrhage segmentation using a deep convolutional model
MD Hssayeni, MS Croock, AD Salman, HF Al-Khafaji… - Data, 2020 - mdpi.com
Traumatic brain injuries may cause intracranial hemorrhages (ICH). ICH could lead to
disability or death if it is not accurately diagnosed and treated in a time-sensitive procedure …
disability or death if it is not accurately diagnosed and treated in a time-sensitive procedure …
Improving sensitivity on identification and delineation of intracranial hemorrhage lesion using cascaded deep learning models
Highly accurate detection of the intracranial hemorrhage without delay is a critical clinical
issue for the diagnostic decision and treatment in an emergency room. In the context of a …
issue for the diagnostic decision and treatment in an emergency room. In the context of a …
A fast and fully-automated deep-learning approach for accurate hemorrhage segmentation and volume quantification in non-contrast whole-head CT
This project aimed to develop and evaluate a fast and fully-automated deep-learning
method applying convolutional neural networks with deep supervision (CNN-DS) for …
method applying convolutional neural networks with deep supervision (CNN-DS) for …
Intracerebral haemorrhage segmentation in non-contrast CT
Abstract A 3-dimensional (3D) convolutional neural network is presented for the
segmentation and quantification of spontaneous intracerebral haemorrhage (ICH) in non …
segmentation and quantification of spontaneous intracerebral haemorrhage (ICH) in non …
Deep learning for automated measurement of hemorrhage and perihematomal edema in supratentorial intracerebral hemorrhage
R Dhar, GJ Falcone, Y Chen, A Hamzehloo, EP Kirsch… - Stroke, 2020 - Am Heart Assoc
Background and Purpose—Volumes of hemorrhage and perihematomal edema (PHE) are
well-established biomarkers of primary and secondary injury, respectively, in spontaneous …
well-established biomarkers of primary and secondary injury, respectively, in spontaneous …
An unsupervised domain adaptation brain CT segmentation method across image modalities and diseases
Computed tomography (CT) is the primary diagnostic tool for brain diseases. To determine
the appropriate treatment plan, it is necessary to ascertain the patient's bleeding volume …
the appropriate treatment plan, it is necessary to ascertain the patient's bleeding volume …
Automatic segmentation of intracerebral hemorrhage in CT images using encoder–decoder convolutional neural network
Intracerebral hemorrhage (ICH) is the most serious type of stroke, which results in a high
disability or mortality rate. Therefore, accurate and rapid ICH region segmentation is of great …
disability or mortality rate. Therefore, accurate and rapid ICH region segmentation is of great …