Deep Learning Algorithms for FreeSurfer

FreeSurfer 的深度学习算法

• 批准号: 10383677
• 负责人: Bruce Fischl
• 金额: $ 66.34万
• 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/10383677
• 关键词: Aging; Algorithms; 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; Neurobiology; Pattern; Phase II Clinical Trials; Phase III Clinical Trials; Physiology; Population; Procedures; Publishing; 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; base; contrast imaging; convolutional neural network; cranium; deep learning; deep learning algorithm; early detection biomarkers; flexibility; high resolution imaging; human disease; improved; large datasets; morphometry; nervous system disorder; neuroimaging; novel; open source; open source tool; prevent; prototype; 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.

摘要 FreeSurfer是一种用于分析磁共振成像（MRI）的工具，已被证明是 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，对人类大脑解剖，连接，脉管系统，化学 组成、生理和功能。在过去的20年里，这些开源工具一直是 开发了精确和自动分割大脑结构阵列的技术， 阿尔茨海默病神经成像计划（ADNI）的核心分析基础设施。在这 项目，我们寻求资源，从根本上提高速度，准确性和灵活性，这些 工具，利用深度学习中令人兴奋的新成果。这将使我们能够更多地 准确量化神经解剖学变化，这对诊断、分期和 评估潜在的治疗干预措施对阿尔茨海默氏症等疾病的疗效。这 包括生成文档、教程、单元测试、回归测试和系统测试 强化这些工具，让临床医生和神经科学家可以使用， 向40，000多名员工分发和支持数据、手册标签和工具， 通过我们现有的开源机制使用FreeSurfer的研究人员。此外，我们会 分析整个阿尔茨海默病神经成像计划数据集并将其返回给公众 包括一组手动标记的数据，可用于优化深度学习工具 在未来十年里治疗老年痴呆症