  Background: Accurate volumetric assessment of spontaneous subarachnoid
hemorrhage (SAH) is a labor-intensive task performed with current manual and
semiautomatic methods that might be relevant for its clinical and prognostic
implications. In the present research, we sought to develop and validate an
artificial intelligence-driven, fully automated blood segmentation tool for SAH
patients via noncontrast computed tomography (NCCT) scans employing a
transformer-based Swin UNETR architecture. Methods: We retrospectively analyzed
NCCT scans from patients with confirmed aneurysmal subarachnoid hemorrhage
(aSAH) utilizing the Swin UNETR for segmentation. The performance of the
proposed method was evaluated against manually segmented ground truth data
using metrics such as Dice score, intersection over union (IoU), the volumetric
similarity index (VSI), the symmetric average surface distance (SASD), and
sensitivity and specificity. A validation cohort from an external institution
was included to test the generalizability of the model. Results: The model
demonstrated high accuracy with robust performance metrics across the internal
and external validation cohorts. Notably, it achieved high Dice coefficient
(0.873), IoU (0.810), VSI (0.840), sensitivity (0.821) and specificity (0.996)
values and a low SASD (1.866), suggesting proficiency in segmenting blood in
SAH patients. The model's efficiency was reflected in its processing speed,
indicating potential for real-time applications. Conclusions: Our Swin
UNETR-based model offers significant advances in the automated segmentation of
blood after aSAH on NCCT images. Despite the computational intensity, the model
operates effectively on standard hardware with a user-friendly interface,
facilitating broader clinical adoption. Further validation across diverse
datasets is warranted to confirm its clinical reliability.
