A deep learning model for automatic segmentation of intraparenchymal and intraventricular hemorrhage for catheter puncture path planning
G Tong, X Wang, H Jiang, A Wu… - IEEE journal of …, 2023 - ieeexplore.ieee.org
Intracerebral hemorrhage is the subtype of stroke with the highest mortality rate, especially
when it also causes secondary intraventricular hemorrhage. The optimal surgical option for …
when it also causes secondary intraventricular hemorrhage. The optimal surgical option for …
Extending the U-Net Architecture for Strokes Segmentation on CT Scan Images
Brain stroke is the second leading cause of death worldwide after heart disease, and one of
the most concerning types is intracranial hemorrhage (ICH). This type of bleeding, caused …
the most concerning types is intracranial hemorrhage (ICH). This type of bleeding, caused …
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 …
[HTML][HTML] Hemorrhagic stroke lesion segmentation using a 3D U-Net with squeeze-and-excitation blocks
V Abramova, A Clerigues, A Quiles… - … Medical Imaging and …, 2021 - Elsevier
Hemorrhagic stroke is the condition involving the rupture of a vessel inside the brain and is
characterized by high mortality rates. Even if the patient survives, stroke can cause …
characterized by high mortality rates. Even if the patient survives, stroke can cause …
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 …
Multi-scale Perception and Feature Refinement Network for multi-class segmentation of intracerebral hemorrhage in CT images
Y Xiao, Y Hou, Z Wang, Y Zhang, X Li, K Hu… - … Signal Processing and …, 2024 - Elsevier
Intracerebral hemorrhage (ICH) poses a severe threat to human health and well-being.
Automatic segmentation of hematomas in CT images can provide essential diagnostic …
Automatic segmentation of hematomas in CT images can provide essential diagnostic …
[HTML][HTML] Deep network for the automatic segmentation and quantification of intracranial hemorrhage on CT
Background The ABC/2 method is usually applied to evaluate intracerebral hemorrhage
(ICH) volume on computed tomography (CT), although it might be inaccurate and not …
(ICH) volume on computed tomography (CT), although it might be inaccurate and not …
Affinity graph based end-to-end deep convolutional networks for ct hemorrhage segmentation
Brain hemorrhage segmentation in Computed Tomography (CT) scan images is
challenging, due to low image contrast and large variations of hemorrhages in appearance …
challenging, due to low image contrast and large variations of hemorrhages in appearance …
[HTML][HTML] A symmetric prior knowledge based deep learning model for intracerebral hemorrhage lesion segmentation
M Nijiati, A Tuersun, Y Zhang, Q Yuan, P Gong… - Frontiers in …, 2022 - frontiersin.org
Background: Accurate localization and classification of intracerebral hemorrhage (ICH)
lesions are of great significance for the treatment and prognosis of patients with ICH. The …
lesions are of great significance for the treatment and prognosis of patients with ICH. The …
ICHNet: intracerebral hemorrhage (ICH) segmentation using deep learning
We develop a deep learning approach for automated intracerebral hemorrhage (ICH)
segmentation from 3D computed tomography (CT) scans. Our model, ICHNet, evolves by …
segmentation from 3D computed tomography (CT) scans. Our model, ICHNet, evolves by …