Brain Digital Slide Archive: An Open Source Platform for data sharing and analysis of digital neuropathology

Brain Digital Slide Archive：数字神经病理学数据共享和分析的开源平台

• 批准号: 10735564
• 负责人: Lee Cooper
• 金额: $ 220.95万
• 依托单位: EMORY UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-09-19 至 2025-08-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10735564
• 关键词: Address; Adopted; Agreement; Algorithmic Analysis; Algorithms; Alzheimer' s Disease; Alzheimer' s disease diagnosis; Alzheimer' s disease related dementia; Archives; Brain; Brain imaging; Brain region; California; Categories; Clinical; Collaborations; Communities; Complement; Computer Vision Systems; Computer software; Data; Data Analyses; Data Set; Development; Diagnosis; Diagnostic; Engineering; Ensure; Evaluation; FAIR principles; Foundations; Funding; Geographic Locations; Geography; Grant; Heterogeneity; Histologic; Histology; Human; Image; Image Analysis; Imaging technology; Individual; Informatics; Infrastructure; Institution; Legal; Libraries; Link; Location; Machine Learning; Magnetic Resonance Imaging; Malignant Neoplasms; Manuals; Manufacturer; Medical Imaging; Metadata; Methods; Microscopic; Modeling; Monitor; Names; National Cancer Institute; Nerve Degeneration; Neurofibrillary Tangles; Nomenclature; Occupations; Pathology; Pattern; Peer Review; Privacy; Process; Radiology Specialty; Readability; Research; Research Personnel; Resources; Running; Scheme; Science; Secure; Security; Site; Slide; Staging; Stains; Standardization; Surveys; System; Technology; Testing; The Cancer Genome Atlas; Tissues; Training; United States National Institutes of Health; Universities; Visualization; advanced analytics; brain tissue; data dictionary; data quality; data sharing; data standards; design; digital; digital imaging; file format; imaging system; improved; innovation; machine learning algorithm; machine learning model; metadata standards; microscopic imaging; multidisciplinary; neuropathology; open source; open source tool; public health relevance; sharing platform; statistics; tool; web platform; web-based tool; whole slide imaging

Recent advances in machine learning and computer vision have had transformative effects on the medical imaging field. Algorithms can now automatically identify patterns and objects in images, often with a degree of precision rivaling human experts for certain tasks. Key to these advances is the availability of large, well-curated datasets in machine-readable formats. Neuropathologic evaluation of brain tissue is central to the diagnosis and staging of Alzheimer's Disease (AD) AD and Related Dementias (AD/ADRDs) but the underlying histology data is not widely and easily shared. The increasing availability of whole slide imaging systems now makes the distribution of histologic data simpler and enables image analysis algorithms to be developed and applied, but numerous barriers exist before such technology can be widely adopted by the neurodegenerative research community. The lack of standard file formats and naming schemas, ensuring subject privacy, subject de-identification, and the enormous size of these images are ongoing challenges. Through NCI/NIH U24 and U01 grants focused on cancer-related image analysis workflows, we have previously developed the Digital Slide Archive (DSA). In this project, we propose to enhance the DSA platform with functionality geared specifically for the neurodegenerative neuropathology community, creating a federated open-source Brain Digital Slide Archive (BDSA) platform. The BDSA is designed to allow the seamless sharing of imaging data, annotations, and metadata amongst participating sites, and to enable the training and deployment of image analysis algorithms on multi-institutional data sets. This includes developing a standardized data dictionary to describe slide-level metadata, and tooling to facilitate data cleanup. We will test these tools and infrastructure by conducting various proof of principal analysis workflows. These include the ability to centrally discover and annotate images stored in geographically distinct regions and run algorithms to identify neurofibrillary tangles (NFTs) using slides from 4 distinct geographic sites (Emory University, University of California Davis, University of Pittsburgh, and Northwestern University) digitized using multiple scanner models and manufacturers. The system will also allow users to securely transfer images to a central location, which may be necessary for certain analytic workflows. These objectives, paired with our complimentary and synergistic expertise in informatics, neuropathology, and engineering, will aid in the development of robust, scalable, reliable, and shareable platforms to provide a foundation for innovative and transformative science addressing a critical unmet need in AD/ADRD research.

机器学习和计算机视觉的最新进展对医学成像领域产生了变革性的影响。算法现在可以自动识别图像中的图案和物体，在某些任务中，其精度通常可以与人类专家相媲美。这些进步的关键是以机器可读格式提供大型，精心策划的数据集。脑组织的神经病理学评价是阿尔茨海默病（AD）AD和相关痴呆（AD/ADRD）的诊断和分期的核心，但基础的组织学数据并不广泛和容易共享。全载玻片成像系统的日益可用性现在使得组织学数据的分布更简单，并且使得图像分析算法能够被开发和应用，但是在这种技术被神经退行性研究界广泛采用之前存在许多障碍。缺乏标准的文件格式和命名模式，确保受试者隐私，受试者去识别，以及这些图像的巨大尺寸是持续的挑战。通过NCI/NIH的U24和U 01赠款专注于癌症相关的图像分析工作流程，我们之前已经开发了数字幻灯片存档（DSA）。在这个项目中，我们建议增强DSA平台的功能，专门针对神经退行性神经病理学社区，创建一个联合的开源脑数字幻灯片存档（BDSA）平台。BDSA旨在允许参与研究中心之间无缝共享成像数据、注释和元数据，并支持在多机构数据集上培训和部署图像分析算法。这包括开发一个标准化的数据字典来描述幻灯片级别的元数据，以及方便数据清理的工具。我们将通过执行各种主要分析工作流程来测试这些工具和基础设施。这些功能包括集中发现和注释存储在不同地理区域的图像的能力，以及使用来自4个不同地理位置（埃默里大学、加州戴维斯大学、匹兹堡大学和西北大学）的切片（使用多个扫描仪型号和制造商进行数字化）运行算法以识别神经纤维缠结（NFT）的能力。该系统还将允许用户安全地将图像传输到中央位置，这可能是某些分析工作流程所必需的。这些目标，再加上我们在信息学，神经病理学和工程学方面的互补和协同专业知识，将有助于开发强大，可扩展，可靠和可共享的平台，为创新和变革性科学提供基础，解决AD/ADRD研究中关键的未满足需求。