Deep Learning Algorithms for FreeSurfer

FreeSurfer 的深度学习算法

基本信息

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
项目类别：
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&apos 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

项目摘要

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，涉及人脑解剖学、连通性、脉管系统、化学在过去的 20 年里，这些开源工具已经在组成、生理学和功能方面得到了广泛的应用。开发出能够准确自动分割一系列大脑结构的技术，并已成为阿尔茨海默病神经影像计划 (ADNI) 的核心分析基础设施。项目中，我们寻求资源来从根本上提高这些项目的速度、准确性和灵活性工具，利用深度学习中令人兴奋的新成果，这将使我们能够获得更多成果。准确量化对诊断、分期和治疗至关重要的神经解剖学变化评估阿尔茨海默病等疾病的潜在治疗干预措施的功效。包括生成文档、教程、单元测试、回归测试和系统测试强化工具并使其可供殖民者和神经科学家使用，最后向超过 40,000 人分发和支持数据、手动标签和工具此外，我们还将通过我们现有的开源机制使用 FreeSurfer 的研究人员。分析整个阿尔茨海默病神经影像计划数据集并将其返回给公众发布，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针对阿尔茨海默病。