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.

利用 Swin UNETR 的基于人工智能的完全自动化血液分割工具，该研究开发和验证了针对非对比增强 CT（NCCT）扫描的 SAH 患者的自发性蛛网膜下腔出血（SAH）的准确容积评估方法，其效果鲁棒且具有广泛的临床可采用性。

基于 Swin Transformers 的全自动流水线用于脑动脉瘤硬膜下出血后头部 CT 扫描的基于深度学习的血液分割

A Fully Automated Pipeline Using Swin Transformers for Deep  Learning-Based Blood Segmentation on Head CT Scans After Aneurysmal  Subarachnoid Hemorrhage

PURPOSE: Subarachnoid hemorrhage (SAH) entails high morbidity and mortality
rates. Convolutional neural networks (CNN), a form of deep learning, are
capable of generating highly accurate predictions from imaging data. Our
objective was to predict mortality in SAH patients by processing the initial CT
scan on a CNN based algorithm.
METHODS: Retrospective multicentric study of a consecutive cohort of patients
with SAH between 2011-2022. Demographic, clinical and radiological variables
were analyzed. Pre-processed baseline CT scan images were used as the input for
training a CNN using AUCMEDI Framework. Our model's architecture leverages the
DenseNet-121 structure, employing transfer learning principles. The output
variable was mortality in the first three months. Performance of the model was
evaluated by statistical parameters conventionally used in studies involving
artificial intelligence methods.
RESULTS: Images from 219 patients were processed, 175 for training and
validation of the CNN and 44 for its evaluation. 52%(115/219) of patients were
female, and the median age was 58(SD=13.06) years. 18.5%(39/219) were
idiopathic SAH. Mortality rate was 28.5%(63/219). The model showed good
accuracy at predicting mortality in SAH patients exclusively using the images
of the initial CT scan (Accuracy=74%, F1=75% and AUC=82%). CONCLUSION: Modern
image processing techniques based on AI and CNN make possible to predict
mortality in SAH patients with high accuracy using CT scan images as the only
input. These models might be optimized by including more data and patients
resulting in better training, development and performance on tasks which are
beyond the skills of conventional clinical knowledge.

使用基于卷积神经网络的算法通过处理初始 CT 扫描图像，能够以高准确度预测蛛网膜下腔出血患者的死亡率。