Deep Learning Algorithms for FreeSurfer

FreeSurfer 的深度学习算法

• 批准号: 10613469
• 负责人: Bruce Fischl
• 金额: $ 71.16万
• 依托单位: MASSACHUSETTS GENERAL HOSPITAL
• 依托单位国家: 美国
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-04-15 至 2025-03-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10613469
• 关键词: Aging; Algorithms; Alzheimer disease detection; Alzheimer' s Disease; Anatomy; Brain; Chemicals; Code; Communities; Data; Data Set; Diagnosis; Disease; Documentation; Engineering; Ensure; Excision; Functional Magnetic Resonance Imaging; Future; Generations; Hour; Human; Image; Infrastructure; Label; Licensing; Magnetic Resonance Imaging; Manuals; Measures; Memory; Modeling; Neuroanatomy; Neurobiology; Pattern; Phase; Phase III Clinical Trials; Physiology; Population; Procedures; Publishing; Qualifying; Recording of previous events; Reproducibility; Research; Research Personnel; Resolution; Resources; Rest; Sensitivity and Specificity; Speed; Staging; Stream; Structure; Surface; System; Technology; Test Result; Testing; Therapeutic Intervention; Time; Training; Validation; Variant; Work; automated segmentation; convolutional neural network; cranium; deep learning; deep learning algorithm; early detection biomarkers; efficacy evaluation; flexibility; high resolution imaging; human disease; improved; large datasets; morphometry; nervous system disorder; neuroimaging; novel; open source; open source tool; prevent; prototype; rapid detection; skills; spatial relationship; support tools; tool; usability; web site; wiki

Abstract FreeSurfer is a tool for the analysis of Magnetic Resonance Imaging (MRI) that has proven to be a flexible and powerful technology for quantifying the effects of many conditions, including numerous neurological disorders, on human brain anatomy, connectivity, vasculature, chemical composition, physiology and function. In the past 20 years, these open source tools have been developed to accurately and automatically segment an array of brain structures and have become the core analysis infrastructure for the Alzheimer’s Disease NeuroImaging Initiative (ADNI). In this project, we seek the resources to radically increase the speed, accuracy and flexibility of these tools, taking advantage of exciting new results in Deep Learning. This will enable us to more accurately quantify neuroanatomical changes that are critical to diagnosing, staging and assessing the efficacy of potential therapeutic interventions in diseases such as Alzheimer’s. This includes the generation of documentation, tutorials, unit tests, regression tests and system tests to harden the tools and make them usable by clinicians and neuroscientists, and finally the distribution and support of the data, manual labelings and tools to the more than 40,000 researchers that use FreeSurfer through our existing open source mechanism. In addition, we will analyze the entire Alzheimer’s Disease NeuroImaging Initiative dataset and return it for public release, including a set of manually labeled data that can be used to optimize Deep Learning tools for Alzheimer’s Disease over the next decade.

抽象的 FreeSurfer是用于分析磁共振成像（MRI）的工具，已证明是 一种灵活而强大的技术，用于量化许多条件的影响，包括 许多神经系统疾病，关于人脑解剖学，连通性，脉管系统，化学 组成，生理和功能。在过去的20年中，这些开源工具一直是 开发以准确，自动分割一系列大脑结构，并已成为 阿尔茨海默氏病神经影像学计划（ADNI）的核心分析基础设施。在这个 项目，我们寻求资源从根本上提高这些资源的速度，准确性和灵活性 工具，利用令人兴奋的深度学习结果。这将使我们能够更多 准确量化对诊断，分期和分期至关重要的神经解剖学变化 评估在阿尔茨海默氏病等疾病中潜在的治疗干预措施的效率。这 包括文档，教程，单位测试，回归测试和系统测试的生成 硬化工具并使它们可由临床医生和神经科学家使用，最后是 向超过40,000多个数据，手动标签和工具的分发和支持 通过我们现有的开源机制使用FreeSurfer的研究人员。此外，我们将 分析整个阿尔茨海默氏病神经影像学计划数据集并将其退还为公众 发布，包括一组手动标记的数据，可用于优化深度学习工具 未来十年的阿尔茨海默氏病。