Biomedical Imaging Group Rotterdam, Department of Radiology & Nuclear Medicine, Erasmus MC, University Medical Center Rotterdam, The Netherlands.
Biomedical Imaging Group Rotterdam, Department of Radiology & Nuclear Medicine, Erasmus MC, University Medical Center Rotterdam, The Netherlands.
Comput Med Imaging Graph. 2024 Jul;115:102392. doi: 10.1016/j.compmedimag.2024.102392. Epub 2024 May 1.
Cerebral X-ray digital subtraction angiography (DSA) is a widely used imaging technique in patients with neurovascular disease, allowing for vessel and flow visualization with high spatio-temporal resolution. Automatic artery-vein segmentation in DSA plays a fundamental role in vascular analysis with quantitative biomarker extraction, facilitating a wide range of clinical applications. The widely adopted U-Net applied on static DSA frames often struggles with disentangling vessels from subtraction artifacts. Further, it falls short in effectively separating arteries and veins as it disregards the temporal perspectives inherent in DSA. To address these limitations, we propose to simultaneously leverage spatial vasculature and temporal cerebral flow characteristics to segment arteries and veins in DSA. The proposed network, coined CAVE, encodes a 2D+time DSA series using spatial modules, aggregates all the features using temporal modules, and decodes it into 2D segmentation maps. On a large multi-center clinical dataset, CAVE achieves a vessel segmentation Dice of 0.84 (±0.04) and an artery-vein segmentation Dice of 0.79 (±0.06). CAVE surpasses traditional Frangi-based k-means clustering (P < 0.001) and U-Net (P < 0.001) by a significant margin, demonstrating the advantages of harvesting spatio-temporal features. This study represents the first investigation into automatic artery-vein segmentation in DSA using deep learning. The code is publicly available at https://github.com/RuishengSu/CAVE_DSA.
脑 X 射线数字减影血管造影（DSA）是神经血管疾病患者中广泛使用的成像技术，可实现高时空分辨率的血管和血流可视化。DSA 中的自动动静脉分割在血管分析中具有重要作用，可以提取定量生物标志物，促进广泛的临床应用。广泛采用的 U-Net 应用于静态 DSA 帧时，常常难以将血管与减影伪影分离。此外，它在有效分离动脉和静脉方面效果不佳，因为它忽略了 DSA 固有的时间透视。为了解决这些限制，我们建议同时利用空间血管和时间大脑血流特征来分割 DSA 中的动脉和静脉。所提出的网络被命名为 CAVE，使用空间模块对 2D+时间 DSA 系列进行编码，使用时间模块聚合所有特征，并将其解码为 2D 分割图。在一个大型多中心临床数据集上，CAVE 实现了血管分割 Dice 为 0.84（±0.04）和动静脉分割 Dice 为 0.79（±0.06）。CAVE 显著优于传统的基于 Frangi 的 k-means 聚类（P < 0.001）和 U-Net（P < 0.001），证明了提取时空特征的优势。这项研究代表了使用深度学习对 DSA 中的自动动静脉分割的首次研究。代码可在 https://github.com/RuishengSu/CAVE_DSA 上获得。
