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.

翻译：背景：自发性蛛网膜下腔出血（SAH）的血肿精确容积评估是一项依赖现有手动及半自动方法的劳动密集型任务，而其临床及预后意义可能极为重要。本研究旨在开发并验证一种基于人工智能的全自动SAH患者血肿分割工具，该工具通过非对比增强CT（NCCT）扫描，采用基于Transformer的Swin UNETR架构。方法：我们回顾性分析经确诊的动脉瘤性蛛网膜下腔出血（aSAH）患者的NCCT扫描数据，使用Swin UNETR进行分割。以手动分割的金标准数据为参照，采用Dice系数、交并比（IoU）、容积相似性指数（VSI）、对称平均表面距离（SASD）以及灵敏度和特异度等指标评估该方法性能。纳入外部机构的验证队列以检验模型泛化能力。结果：该模型在内部和外部验证队列中均表现出高精度与稳健性能指标。值得注意的是，其Dice系数（0.873）、IoU（0.810）、VSI（0.840）、灵敏度（0.821）和特异度（0.996）均较高，SASD较低（1.866），表明模型在SAH患者血肿分割方面具有卓越能力。模型效率体现在处理速度上，提示其具备实时应用潜力。结论：我们基于Swin UNETR的模型在NCCT图像的aSAH后血肿自动分割方面取得显著进展。尽管计算强度较高，但该模型可在标准硬件上高效运行并配备用户友好界面，有助于推动临床广泛采用。未来需通过多样化数据集进一步验证其临床可靠性。