Segmentation and Approximation of Blood Volume in Intracranial Hemorrhage Patients Based on Computed Tomography Scan Images Using Deep Learning Method

Irene, Kezia; Ma’sum, M. Anwar; Yunus, Reyhan Eddy; Jatmiko, Wisnu

doi:10.1109/iwbis50925.2020.9255593

Cited by 10 publications

(13 citation statements)

References 21 publications

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“…Dawud et al only reported the third parameter, “accuracy,” which was 93.48%. 16 The DL model with the highest sensitivity was of Cho et al, 17 followed that of by Irene et al 12…”

Section: Resultsmentioning

confidence: 84%

Automated Detection of Intracranial Hemorrhage from Head CT Scans Applying Deep Learning Techniques in Traumatic Brain Injuries: A Comparative Review

Agrawal

Poonamallee

Joshi³

2023

Indian Journal of Neurotrauma

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Traumatic brain injury (TBI) is not only an acute condition but also a chronic disease with long-term consequences. Intracranial hematomas are considered the primary consequences that occur in TBI and may have devastating effects that may lead to mass effect on the brain and eventually cause secondary brain injury. Emergent detection of hematoma in computed tomography (CT) scans and assessment of three major determinants, namely, location, volume, and size, is crucial for prognosis and decision-making, and artificial intelligence (AI) using deep learning techniques, such as convolutional neural networks (CNN) has received extended attention after demonstrations that it could perform at least as well as humans in imaging classification tasks. This article conducts a comparative review of medical and technological literature to update and establish evidence as to how technology can be utilized rightly for increasing the efficiency of the clinical workflow in emergency cases. A systematic and comprehensive literature search was conducted in the electronic database of PubMed and Google Scholar from 2013 to 2023 to identify studies related to the automated detection of intracranial hemorrhage (ICH). Inclusion and exclusion criteria were set to filter out the most relevant articles. We identified 15 studies on the development and validation of computer-assisted screening and analysis algorithms that used head CT scans. Our review shows that AI algorithms can prioritize radiology worklists to reduce time to screen for ICH in the head scans sufficiently and may also identify subtle ICH overlooked by radiologists, and that automated ICH detection tool holds promise for introduction into routine clinical practice.

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“…Dawud et al only reported the third parameter, “accuracy,” which was 93.48%. 16 The DL model with the highest sensitivity was of Cho et al, 17 followed that of by Irene et al 12…”

Section: Resultsmentioning

confidence: 84%

Automated Detection of Intracranial Hemorrhage from Head CT Scans Applying Deep Learning Techniques in Traumatic Brain Injuries: A Comparative Review

Agrawal

Poonamallee

Joshi³

2023

Indian Journal of Neurotrauma

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“…While previous machine learning methods have been used to detect and classify intracranial haemorrhages [36,37], by providing saliency maps highlighting probable regions where blood is distributed [38], these methods do not produce segmentations from which precise blood volumes can be obtained. Quantitative volumetric segmentations have typically been applied to haemorrhagic lesions from traumatic brain injuries, with focus on subdural haematoma, extradural haematoma, and intraparenchymal haemorrhage [39][40][41][42][43]. Less work has attempted the automated segmentation of subarachnoid blood [37], and to our knowledge this work represents the first use of machine learning techniques to segment blood from CT head scans in aneurysmal SAH patients.…”

Section: Interpretation and Contextmentioning

confidence: 99%

Predicting vasospasm risk using first presentation aneurysmal subarachnoid haemorrhage volume: a semi-automated CT image segmentation analysis in ITK-SNAP

Street

Pandit

Toma

2023

Preprint

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PurposeCerebral vasospasm following aneurysmal subarachnoid haemorrhage (aSAH) is a significant complication associated with poor neurological outcomes. We present a novel, semi-automated pipeline in ITK-SNAP to segment subarachnoid blood volume from initial CT head (CTH) scans and use this to predict future radiological vasospasm.Methods42 patients were admitted between February 2020 and December 2021 to our tertiary neurosciences centre, and whose initial referral CTH scan was used for this retrospective cohort study. Blood load was segmented using a semi-automated random forest classifier and active contour evolution implemented in the open-source medical imaging analysis software ITK-SNAP. Clinical data were extracted from electronic healthcare records in order to fit models aimed at predicting radiological vasospasm risk.ResultsSemi-automated segmentations demonstrated excellent agreement with manual, expert-derived volumes (mean Dice coefficient=0.92). Total normalised blood volume, extracted from CTH images at first presentation, was significantly associated with greater odds of later radiological vasospasm, increasing by approximately 7% for each additional cm3of blood (OR=1.069, 95% CI: 1.021-1.120; p<.005). Greater blood volume was also significantly associated with vasospasm of a higher Lindegaard ratio, of longer duration, and a greater number of discrete episodes. Total blood volume predicted radiological vasospasm with a greater accuracy as compared to the modified Fisher scale (AUC= 0.86 vs 0.70), and was of independent predictive value.ConclusionSemi-automated methods provide a plausible pipeline for the segmentation of blood from CT head images in aSAH, and total blood volume is a robust, extendable predictor of radiological vasospasm, outperforming the modified Fisher scale. Greater subarachnoid blood volume significantly increases the odds of subsequent vasospasm, its time course and its severity.

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“…Their ensemble achieved an area under the receiver operating characteristic curve (AUC) of 0.87 [24]. Irene et al applied a combination of deep learning classification and regression methods to automate the detection, segmentation and volume approximation of ICH, with a system sensitivity and specificity of 0.97 and 0.96, respectively [25]. Kuo et al trained a deep learning algorithm to detect and segment ICH using a dataset consisting of 4,396 NCCTB scans.…”

Section: The Current State Of Machine Learning Applied To Ctb Datamentioning

confidence: 99%

Charting the potential of brain computed tomography deep learning systems

Buchlak¹,

Milne²,

Seah³

et al. 2022

Journal of Clinical Neuroscience

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Segmentation and Approximation of Blood Volume in Intracranial Hemorrhage Patients Based on Computed Tomography Scan Images Using Deep Learning Method

Cited by 10 publications

References 21 publications

Automated Detection of Intracranial Hemorrhage from Head CT Scans Applying Deep Learning Techniques in Traumatic Brain Injuries: A Comparative Review

Automated Detection of Intracranial Hemorrhage from Head CT Scans Applying Deep Learning Techniques in Traumatic Brain Injuries: A Comparative Review

Predicting vasospasm risk using first presentation aneurysmal subarachnoid haemorrhage volume: a semi-automated CT image segmentation analysis in ITK-SNAP

Charting the potential of brain computed tomography deep learning systems

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