The circle of Willis (CoW) is a network of cerebral arteries with significant inter-individual anatomical variations. Deep learning has been used to characterize and quantify the status of the CoW in various applications for the diagnosis and treatment of cerebrovascular disease. In medical imaging, the performance of deep learning models is limited by the diversity and size of training datasets. To address medical data scarcity, generative adversarial networks (GANs) have been applied to generate synthetic vessel neuroimaging data. However, the proposed methods produce synthetic data with limited anatomical fidelity or downstream utility in tasks concerning vessel characteristics.

中文翻译：

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使用 3D-StyleGAN 对威利斯环进行生成建模

威利斯环 (CoW) 是一个脑动脉网络，具有显着的个体间解剖差异。深度学习已被用来表征和量化 CoW 在脑血管疾病诊断和治疗的各种应用中的状态。在医学成像中，深度学习模型的性能受到训练数据集的多样性和大小的限制。为了解决医疗数据稀缺的问题，生成对抗网络（GAN）已被应用于生成合成血管神经影像数据。然而，所提出的方法产生的合成数据在涉及血管特征的任务中解剖保真度或下游效用有限。