School of Computer Science and Engineering, Beihang University, Beijing, China.
State Key Laboratory of Virtual Reality Technology and Systems, Beihang University, Beijing, China.
Interdiscip Sci. 2024 Mar;16(1):58-72. doi: 10.1007/s12539-023-00583-x. Epub 2023 Aug 26.
Stroke is still the World's second major factor of death, as well as the third major factor of death and disability. Ischemic stroke is a type of stroke, in which early detection and treatment are the keys to preventing ischemic strokes. However, due to the limitation of privacy protection and labeling difficulties, there are only a few studies on the intelligent automatic diagnosis of stroke or ischemic stroke, and the results are unsatisfactory. Therefore, we collect some data and propose a 3D carotid Computed Tomography Angiography (CTA) image segmentation model called CA-UNet for fully automated extraction of carotid arteries. We explore the number of down-sampling times applicable to carotid segmentation and design a multi-scale loss function to resolve the loss of detailed features during the process of down-sampling. Moreover, based on CA-Unet, we propose an ischemic stroke risk prediction model to predict the risk in patients using their 3D CTA images, electronic medical records, and medical history. We have validated the efficacy of our segmentation model and prediction model through comparison tests. Our method can provide reliable diagnoses and results that benefit patients and medical professionals.
中风仍然是世界上第二大致死因素，也是第三大致死和致残因素。缺血性中风是中风的一种类型，早期发现和治疗是预防缺血性中风的关键。然而，由于隐私保护和标记困难的限制，对于中风或缺血性中风的智能自动诊断的研究很少，并且结果并不令人满意。因此，我们收集了一些数据，并提出了一个名为 CA-Unet 的 3D 颈动脉计算机断层血管造影（CTA）图像分割模型，用于全自动提取颈动脉。我们探索了适用于颈动脉分割的下采样次数，并设计了一个多尺度损失函数，以解决下采样过程中详细特征的丢失问题。此外，基于 CA-Unet，我们提出了一种缺血性中风风险预测模型，利用患者的 3D CTA 图像、电子病历和病史来预测风险。我们通过对比测试验证了我们的分割模型和预测模型的功效。我们的方法可以为患者和医疗专业人员提供可靠的诊断和结果。
