Purpose Cerebral 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. Methods 42 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. Results Semi-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 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=0.84 vs 0.72), however both had independent predictive value. Conclusion Semi-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.

The authors have declared no competing interest.

AKT is supported by the UCLH Biomedical Research Centre. JSS and ASP received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors for this work.

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Local institutional review board of the National Hospital of Neurology and Neurosurgery, London, UK (University College Hospitals NHS Trust) gave ethical approval for this work (53-202122-CA).

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Due to the sensitive nature of the data included in this research, supporting imaging data are not available. Analysis code and pipelines are available from the corresponding author upon reasonable request.