ArticlePloS one2023
Predicting vasospasm risk using first presentation aneurysmal subarachnoid hemorrhage volume: A semi-automated CT image segmentation analysis using ITK-SNAP.
Article in PloS one, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers, 1 of them a synthesis that pooled it.
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10 citing papers in PubMed, 1 synthesis or guideline pooled it, 18 citations in OpenAlex.
- Machine learning models for predicting vasospasm following ruptured intracranial aneurysms: a systematic review and meta-analysis.Acta neurochirurgica · 2025Pooled it
- Quantification of blood and CSF volume to predict outcome after aneurysmal subarachnoid hemorrhage.Neurosurgical review · 2024Trial
- LoRA-based methods on Unet for transfer learning in aneurysmal subarachnoid hematoma segmentation.BMC medical imaging · 2025Article
- Volumetric blood cutoff for outcome prediction in aneurysmal subarachnoid hemorrhage using semiautomated quantification.European journal of medical research · 2025Article
- Quantification of hounsfield unit difference in heterogeneous hematoma predicts poor functional outcomes in acute intracerebral hemorrhage.Scientific reports · 2025Observational
- Semiautomated Hemorrhage Volume Quantification in Aneurysmal Subarachnoid Hemorrhage.Neurocritical care · 2025Article
- Mapping Pituitary Neuroendocrine Tumors: An Annotated MRI Dataset Profiling Tumor and Carotid Characteristics.Scientific data · 2025Article
- A Practical Guide to Manual and Semi-Automated Neurosurgical Brain Lesion Segmentation.NeuroSci · 2024Article
- Deep learning-based multiclass segmentation in aneurysmal subarachnoid hemorrhage.Frontiers in neurology · 2024Article
- Correlation between tomographic scales and vasospasm and delayed cerebral ischemia in aneurysmal subarachnoid hemorrhage.Critical care science · 2023Article
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3 authors at 2 institutions in 1 country.
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Abstract
purposeCerebral vasospasm following aneurysmal subarachnoid hemorrhage (aSAH) is a significant complication associated with poor neurological outcomes. We present a novel, semi-automated pipeline, implemented in the open-source medical imaging analysis software 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 center, 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 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 normalized 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 cm3 of 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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