AIMC Topic: Hematoma

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Evaluation of Traumatic Subdural Hematoma Volume by Using Image Segmentation Assessment Based on Deep Learning.

Computational and mathematical methods in medicine
Rapid and accurate evaluations of hematoma volume can guide the treatment of traumatic subdural hematoma. We aim to explore the consistency between the measurement results of traumatic subdural hematoma (TSDH) using a deep learn-based image segmentat...

External validation study on the value of deep learning algorithm for the prediction of hematoma expansion from noncontrast CT scans.

BMC medical imaging
BACKGROUND: Hematoma expansion is an independent predictor of patient outcome and mortality. The early diagnosis of hematoma expansion is crucial for selecting clinical treatment options. This study aims to explore the value of a deep learning algori...

Hematoma Expansion Context Guided Intracranial Hemorrhage Segmentation and Uncertainty Estimation.

IEEE journal of biomedical and health informatics
Accurate segmentation of the Intracranial Hemorrhage (ICH) in non-contrast CT images is significant for computer-aided diagnosis. Although existing methods have achieved remarkable 1 1 The code will be available from https://github.com/JohnleeHIT/SLE...

Advances in computed tomography-based prognostic methods for intracerebral hemorrhage.

Neurosurgical review
Spontaneous intracerebral hemorrhage (ICH) has high morbidity and mortality. Computed tomography (CT) plays an important role in the diagnosis, treatment, and research of cerebrovascular diseases. Non-contrast CT is widely used in the clinical diagno...

Weakly supervised multitask learning models to identify symptom onset time of unclear-onset intracerebral hemorrhage.

International journal of stroke : official journal of the International Stroke Society
BACKGROUND: Approximately one-third of spontaneous intracerebral hemorrhage patients did not know the onset time and were excluded from studies about time-dependent treatments for hyperacute spontaneous intracerebral hemorrhage.

A Robust Deep Learning Segmentation Method for Hematoma Volumetric Detection in Intracerebral Hemorrhage.

Stroke
BACKGROUND AND PURPOSE: Hematoma volume (HV) is a significant diagnosis for determining the clinical stage and therapeutic approach for intracerebral hemorrhage (ICH). The aim of this study is to develop a robust deep learning segmentation method for...

Deep learning for automatically predicting early haematoma expansion in Chinese patients.

Stroke and vascular neurology
BACKGROUND AND PURPOSE: Early haematoma expansion is determinative in predicting outcome of intracerebral haemorrhage (ICH) patients. The aims of this study are to develop a novel prediction model for haematoma expansion by applying deep learning mod...

Association of baseline hematoma and edema volumes with one-year outcome and long-term survival after spontaneous intracerebral hemorrhage: A community-based inception cohort study.

International journal of stroke : official journal of the International Stroke Society
BACKGROUND: Hospital-based studies have reported variable associations between outcome after spontaneous intracerebral hemorrhage and peri-hematomal edema volume.