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:
- 金额:$ 69.99万
- 依托单位:
- 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:
- 关键词:AdoptedAgingAlgorithmsAlzheimer&aposs DiseaseAlzheimer&aposs disease diagnosisAlzheimer&aposs disease modelAnatomyBrainBrain imagingBrain scanClinicalCognitiveComputer AssistedComputer softwareConsumptionDataData AnalysesData SetDementiaDevelopmentDisease ProgressionEnsureEnvironmentEventHigh Performance ComputingHourImageImage AnalysisImaging technologyImpaired cognitionIndividualInterventionKnowledgeLearningMachine LearningMagnetic Resonance ImagingMapsMeasuresMedicineMethodsModelingNeurodegenerative DisordersPattern RecognitionPennsylvaniaPerformancePersonsPrognosisResearchResearch PersonnelResourcesRiskScanningSource CodeSpeedStructureSurfaceTechniquesTimeTranslationsUniversitiesbrain basedbrain morphologybrain tissuecluster computingconvolutional neural networkcostdeep learningdeep learning algorithmdisease prognosisgraph neural networkimage processingimage registrationimaging Segmentationimprovedlarge scale datalearning strategymild cognitive impairmentneuroimagingnon-invasive imagingnovelopen sourceportabilitypre-clinicalpredictive modelingprognostic modelprogramsreconstructionsegmentation algorithmsupervised learningtooluser-friendlyweb 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
项目成果
期刊论文数量(0)
专著数量(0)
科研奖励数量(0)
会议论文数量(0)
专利数量(0)
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Yong Fan其他文献
Yong Fan的其他文献
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{{ truncateString('Yong Fan', 18)}}的其他基金
Personalized Functional Network Modeling to Characterize and Predict Psychopathology in Youth
用于表征和预测青少年精神病理学的个性化功能网络模型
- 批准号:
10304463 - 财政年份:2021
- 资助金额:
$ 69.99万 - 项目类别:
Personalized Functional Network Modeling to Characterize and Predict Psychopathology in Youth
用于表征和预测青少年精神病理学的个性化功能网络模型
- 批准号:
10630919 - 财政年份:2021
- 资助金额:
$ 69.99万 - 项目类别:
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
- 批准号:
10371213 - 财政年份:2021
- 资助金额:
$ 69.99万 - 项目类别:
Personalized Functional Network Modeling to Characterize and Predict Psychopathology in Youth
用于表征和预测青少年精神病理学的个性化功能网络模型
- 批准号:
10460612 - 财政年份:2021
- 资助金额:
$ 69.99万 - 项目类别:
Center for Machine Learning in Urology-Scientific Project
泌尿科机器学习中心科学项目
- 批准号:
10260579 - 财政年份:2020
- 资助金额:
$ 69.99万 - 项目类别:
Individualized Closed Loop TMS for Working Memory Enhancement
用于增强工作记忆的个性化闭环 TMS
- 批准号:
10632147 - 财政年份:2019
- 资助金额:
$ 69.99万 - 项目类别:
Individualized Closed Loop TMS for Working Memory Enhancement
用于增强工作记忆的个性化闭环 TMS
- 批准号:
10417107 - 财政年份:2019
- 资助金额:
$ 69.99万 - 项目类别:
Individualized Closed Loop TMS for Working Memory Enhancement
用于增强工作记忆的个性化闭环 TMS
- 批准号:
10204952 - 财政年份:2019
- 资助金额:
$ 69.99万 - 项目类别:
Individualized Closed Loop TMS for Working Memory Enhancement
用于增强工作记忆的个性化闭环 TMS
- 批准号:
10006111 - 财政年份:2019
- 资助金额:
$ 69.99万 - 项目类别:
Computer Aided Early Detection and Diagnosis of Alzheimer's Disease
计算机辅助阿尔茨海默病的早期检测和诊断
- 批准号:
7707231 - 财政年份:2009
- 资助金额:
$ 69.99万 - 项目类别:
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