Vessel Identification and Tracing in DSA Image Series for Cerebrovascular Surgical Planning

项目摘要 虽然数字减影血管造影术（DSA）是最重要的成像显示脑血管 解剖学，临床医生对其解释仍然很困难。在治疗动静脉疾病时尤其如此 畸形（AVM），其中静脉和动脉缠绕，需要仔细识别艾德。所呈现的 项目旨在增强DSA图像系列，以消除这一困难。我们的长期目标是为 开发直观和可解释的可视化工具，以改善诊断、规划和预防 神经血管病理学我们在这个项目中的总体目标是（i）开发一种新的方法， 机器学习，定位AVM并在DSA图像中区分其周围的静脉和动脉 系列，以及（ii）开发一种算法，将动脉分类为终端，通道或旁观者。此外 通过回顾性研究，检查我们的方法在计划神经血管手术中的影响。的 该项目的基本原理是，这种技术可能会增强DSA成像，并提供一种可解释的工具 临床医生，这将有助于规划脑AVM手术，并进一步提供决策支持 在手术过程中可用于帮助查看和关联手术区域中观察到的解剖学发现的工具 与术前血管造影片吻合为实现总体目标，将努力实现以下两个具体目标： (1)开发用于AVM定位和动脉/静脉分类的图像处理算法，以及（2）开发 算法，可以识别动脉终端，通道或旁观者。在第一个目标下，我们将测试我们的 工作假设，以表明有可能在DSA图像系列中定位AVM，并区分 供血动脉和引流静脉围绕或产生缠结，使用深层神经网络， 在图像中勾勒出AVM的形状，并进行独立成分分析以了解血流 破坏对于第二个目标，我们将建立一套规则来分类是否参与AVM的动脉 并将这些规则实现到将单独跟踪血管的动态实例分割算法中， 在DSA图像系列中。该算法依靠前景/背景减法来生成血管 图和深度神经网络来分类血管接头以产生实例化图。使用此 图和预定义的规则，将有可能在视觉上区分不同的动脉模式。 拟议的项目是创新的，因为它将有可能自动区分静脉和动脉， 在DSA图像系列中将动脉分类为终末、通过或旁观者，而不改变标准临床 例行公事拟议的项目是重要的，因为它将增强DSA成像与直观的可视化 使临床医生能够更好地了解AVM引起的血管缠绕， 术中错误夹闭，从而避免术中夹闭或术后缺陷。这些 这些结果将产生重要的积极影响，因为它们最终将提供新的机会 用于开发新的计划技术，以改善神经血管畸形的治疗。

Project Summary Although Digital Subtraction Angiography (DSA) is the most important imaging for visualizing cerebrovascular anatomy, its interpretation by clinicians remains difficult. This is particularly true when treating arteriovenous malformations (AVMs), where veins and arteries are entangled and need to be carefully identified. The presented project aims at enhancing DSA image series to remove this difficulty. Our long-term goal is to contribute toward the development of intuitive and interpretable visualization tools to improve the diagnosis, planning and treatement of neurovascular pathologies. Our overall objectives in this project are to (i) develop a new method, based on machine learning, to localize the AVM and distinguish between veins and arteries surrounding it in DSA image series, and (ii) develop an algorithm that classifies arteries as terminal, en passage or bystanders. In addition to examine the impact of our approach in planning neurovascular surgeries through a retrospective study. The rationale for this project is that such technology will likely enhance DSA imaging and provide an interpretable tool to clinicians that will facilitate planning cerebral AVM procedures, and furthermore, provide a decision support tool that can be used during surgery to help review and correlate the anatomic findings seen in the surgical field to the preoperative angiogram. To attain the overall objectives, the following two specific aims will be pursued: (1) develop an image processing algorithm for AVM localisation and artery/vein classification and (2) develop an algorithm that can identify arteries as terminal, en passage or bystanders. Under the first aim, we will test our working hypothesis to show that it is possible to localize an AVM in DSA image series and to distinguish between feeding arteries and draining veins surrounding or creating the entanglement, using deep neural networks to outline the shape of the AVM in the images and independent component analysis to understand blood flow disruption. For the second aim, we will establish a set of rules to classify arteries contributing or not to the AVM and implement these rules into a dynamic instance segmentation algorithm that will trace vessels individually, in a DSA image series. This algorithm with rely on a foreground/background subtraction to genrate a vascular graph and on deep neural network to classify vascular junctions to produce an instanciated graph. Using this graph and the pre-defined rules it will be possible to visually distinguish between the different artery patterns. The proposed project is innovative because it will be possible to automatically distinguish veins from arteries and classify arteries as terminal, en passage, or bystander in a DSA image series without altering standard clinical routines. The proposed project is significant because it will enhance DSA imaging with an intuitive visualization allowing clinicians to better understand AVM-induced vessel entanglement in order to preserve vessels from being mistakenly clamped during surgery, thus avoiding intraoperative hemorrhaging or postsurgical deficits. These results are expected to have an important positive impact because they will ultimately provide new opportunities for the development of novel planning techniques to improve the treatment of neurovascular malformations.