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Fast and robust deep learning tools for analysis of neuroimaging data of Alzheimer's disease

快速、强大的深度学习工具，用于分析阿尔茨海默病的神经影像数据

基本信息

批准号：
10573337
负责人：
Yong Fan
金额：
$ 69.99万
依托单位：
UNIVERSITY OF PENNSYLVANIA
依托单位国家：
美国
项目类别：
财政年份：
2021
资助国家：
美国
起止时间：
2021-03-15 至 2026-02-28
项目状态：
未结题

来源：
https://reporter.nih.gov/project-details/10573337
关键词：
Adopted Aging Algorithms Alzheimer&apos s Disease Alzheimer&apos s disease diagnosis Alzheimer&apos s disease model Anatomy Brain Brain imaging Brain scan Clinical Cognitive Computer Assisted Computer software Consumption Data Data Analyses Data Set Dementia Development Disease Progression Ensure Environment Event High Performance Computing Hour Image Image Analysis Imaging technology Impaired cognition Individual Intervention Knowledge Learning Machine Learning Magnetic Resonance Imaging Maps Measures Medicine Methods Modeling Neurodegenerative Disorders Pattern Recognition Pennsylvania Performance Persons Prognosis Research Research Personnel Resources Risk Scanning Source Code Speed Structure Surface Techniques Time Translations Universities brain based brain morphology brain tissue cluster computing convolutional neural network cost deep learning deep learning algorithm disease prognosis graph neural network image processing image registration imaging Segmentation improved large scale data learning strategy mild cognitive impairment neuroimaging non-invasive imaging novel open source portability pre-clinical predictive modeling prognostic model programs reconstruction segmentation algorithm supervised learning tool user-friendly web app

项目摘要

Alzheimer's disease (AD) is the most prevalent neurodegenerative disorder. Interventions at the preclinical and prodromal stages are appealing targets for slowing or halting disease progression. It is desired to achieve accurate prognosis of AD dementia and cognitive decline for people with mild cognitive impairment who have increased risk to develop AD. In order to achieve fast and accurate prognosis of AD dementia based on neuroimaging data, we will develop and validate novel deep learning techniques. Particularly, we will develop unsupervised deep learning methods for segmenting brain images and reconstructing cortical surfaces from structural magnetic resonance imaging data. These fast and accurate image processing methods will be used in conjunction with advanced deep learning methods to build prognosis models of AD dementia and cognitive decline in a time-to-event analysis framework using large-scale imaging datasets. Finally, we will develop and disseminate a user friendly, open source, modular, and extensible software package to improve prognosis of AD dementia. Source code, standalone programs, and web-application interfaces of all the algorithms will be made available on GitHub and NITRC. Our tools will enable real-time neuroimaging data analysis and can find applications in diverse fields, including quantifying brain changes associated with aging and development.

阿尔茨海默病（AD）是最常见的神经退行性疾病。临床前干预