Stroke is one of the leading causes of death and disability worldwide. Reducing this disease burden through drug discovery and evaluation of stroke patient outcomes requires broader characterization of stroke pathophysiology, yet the underlying biologic and genetic factors contributing to outcomes are largely unknown. Remedying this critical knowledge gap requires deeper phenotyping, including large-scale integration of demographic, clinical, genomics and imaging features. Such big data approaches will be facilitated by developing and running processing pipelines to extract stroke-related phenotypes at large scale. Millions of stroke patients undergo routine brain imaging each year, capturing a rich set of data on stroke-related injury and outcomes. The Stroke Neuroimaging Phenotype Repository (SNIPR) was developed as a multi-center centralized imaging repository of clinical CT and MRI scans from stroke patients worldwide, based on the open source XNAT imaging informatics platform. The aims of this repository are to: (i) store, manage, process, and facilitate sharing of high-value stroke imaging data sets, (ii) implement containerized automated computational methods to extract image characteristics and disease-specific features from contributed images, (iii) facilitate integration of imaging, genomics and clinical data to perform large-scale analysis of complications after stroke; and (iv) develop SNIPR as a collaborative platform aimed at both data scientists and clinical investigators. Currently, SNIPR hosts research projects encompassing ischemic and hemorrhagic stroke, with data from 2246 subjects, and 6149 imaging sessions from Washington University’s clinical image archive as well as contributions from collaborators in different countries, including Finland, Poland, and Spain. Moreover, we have extended the XNAT data model to include relevant clinical features, including subject demographics, stroke severity (NIH Stroke Scale), stroke subtype (using TOAST classification) and outcome (modified Rankin Scale). Image processing pipelines are deployed on SNIPR using containerized modules, which facilitate replicability at a large scale. The first such pipeline identifies axial brain CT scans from dicom header data and image data using deep learning classifier, registers serial scans to an atlas, segments tissue compartments, and calculates CSF volume. The resulting volume can be used to quantify the progression of cerebral edema after ischemic stroke to get broad understanding of stroke progression and outcomes.

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中风神经影像表型库 (SNIPR)：中风研究的开放数据科学平台

中风是全球死亡和残疾的主要原因之一。通过药物发现和评估中风患者的预后来减轻这种疾病负担需要更广泛的中风病理生理学特征，但导致预后的潜在生物学和遗传因素在很大程度上是未知的。弥补这一关键的知识差距需要更深入的表型分析，包括人口统计学、临床、基因组学和影像学特征的大规模整合。通过开发和运行处理管道以大规模提取与中风相关的表型，将促进这种大数据方法。每年有数以百万计的中风患者接受常规的脑部成像，获取关于中风相关损伤和结果的丰富数据。中风神经影像表型资料库 (SNIPR) 是基于开源 XNAT 成像信息学平台开发的一个多中心集中式影像资料库，其中包含全球中风患者的临床 CT 和 MRI 扫描。该存储库的目的是：(i) 存储、管理、处理和促进高价值中风成像数据集的共享，(ii) 实施容器化的自动化计算方法，以从提供的图像中提取图像特征和疾病特异性特征， (iii) 促进影像学、基因组学和临床数据的整合，以对中风后并发症进行大规模分析；(iv) 将 SNIPR 开发为面向数据科学家和临床研究人员的协作平台。目前，SNIPR 主持的研究项目包括缺血性和出血性中风，来自华盛顿大学临床图像档案的 2246 名受试者的数据和 6149 次成像会议，以及来自不同国家的合作者的贡献，包括芬兰、波兰和西班牙。此外，我们扩展了 XNAT 数据模型以包括相关的临床特征，包括受试者人口统计学、中风严重程度（NIH 中风量表）、中风亚型（使用 TOAST 分类）和结果（改良 Rankin 量表）。图像处理管道使用容器化模块部署在 SNIPR 上，这有助于大规模复制。第一个这样的管道使用深度学习分类器从 dicom 标题数据和图像数据中识别轴向脑 CT 扫描，将串行扫描注册到图谱，分割组织隔室，并计算 CSF 体积。