Spatial inference methods for image analysis

图像分析的空间推理方法

基本信息

批准号：
10093037
负责人：
Armin Schwartzman
金额：
$ 40.59万
依托单位：
UNIVERSITY OF CALIFORNIA, SAN DIEGO
依托单位国家：
美国
项目类别：
财政年份：
2019
资助国家：
美国
起止时间：
2019-05-15 至 2023-01-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/10093037
关键词：
3-Dimensional Address Adolescence Adolescent Affect Anatomy Beds Brain Brain Mapping Brain region Characteristics Climate Cognitive Collection Computer software Data Data Analyses Detection Electroencephalography Ensure Fiber Follow-Up Studies Functional Imaging Functional Magnetic Resonance Imaging Funding Gaussian model Goals Grant Health Height Image Image Analysis Location Maps Methodology Methods Microscopy Modeling Noise Output Pattern Procedures Reproducibility Research Sample Size Signal Transduction Software Tools Standardization Surface Testing Three-dimensional analysis Time Uncertainty United States National Institutes of Health Work anatomic imaging biobank brain volume cognitive development cognitive function data structure field theory functional gain imaging modality local maxima neuroimaging relating to nervous system simulation spatiotemporal substance use tool user friendly software

项目摘要

From biomedical to environmental research, a central problem in image analysis is to recognize and locate important effects. An archetypal example is image analysis of the 3D brain volume or the 2D cortical surface, using both anatomical and functional imaging. Examples also abound in 1D functional data (EEG patterns or anatomical neural fibers), 2D images (microscopy) and 2D spatial data (climate maps). These problems share a common data structure in which smooth functions or images are observed repeatedly and aligned on a fine grid. The goal of localization is to identify regions where the signal is strong or where differences exist between conditions or groups of subjects. While there is a rich collection of tools to analyze imaging data, the focus has been mainly on significance testing and controlling error rates under the null hypothesis and has been limited by practical but unrealistic assumptions about the noise field, compromising error control and statistical power. On the other hand, the functional data analysis approach rightly works under the non-zero mean model but ignores the analytical power of smooth random field theory, which has been so successful in image analysis and could enable similar gains for functional data. The main goal of this proposal is to develop new spatial inference methods that directly address the estimation of non-sparse signals and quantification of their spatial uncertainty, in order to increase statistical power, control error rates and obtain appropriately interpretable results. In the previous cycle of this grant, we established methodology for formal error control in peak detection. This renewal develops location uncertainty and detection power for peaks (Aim 1), and moves further to develop confidence bands and spatial confidence regions for the entire signal (Aim 2) and for excursion sets where the signal exceeds a threshold (Aim 3). Methods are proposed to target both the mean (effect magnitude) and the signal-to-noise ratio (standardized mean or effect size), allowing interpretable inference in the presence of spatially non- constant variance, characteristic of neuroimaging data. The proposal offers clear definitions of spatial inference, and supports the methodology with smooth Gaussian random field theory, forgoing the stationarity and zero-mean assumptions. These methods are rigorously validated and used to map the cognitive effects of addictive substance use in the large NIH-funded Adolescent Brain Cognitive Development (ABCD) study. This proposal uniquely brings together ideas from image and functional data analysis to provide more accurate and interpretable spatial localization of important effects in smooth signals and images. The methods developed in this proposal offer more accurate mapping of the brain and other domains, and higher statistical power to identify locations where important effects occur, enhancing scientific understanding and guiding better targeted follow-up studies.

从生物医学到环境研究，图像分析的一个中心问题是识别和找到重要的影响。一个典型的例子是 3D 大脑体积或 2D 皮质的图像分析表面，使用解剖和功能成像。一维功能数据（EEG 模式或解剖神经纤维）、2D 图像（显微镜）和 2D 空间数据（气候图）。这些问题共享一个共同的数据结构，其中重复观察平滑函数或图像，并且在精细网格上对齐。定位的目标是识别信号较强的区域或条件或受试者组之间存在差异。虽然有丰富的工具来分析成像数据，但重点主要集中在显着性检验和控制原假设下的错误率，并受到实际的限制但对噪声场的不切实际的假设会损害误差控制和统计能力。上另一方面，函数数据分析方法在非零均值模型下正确工作，但忽略了平滑随机场理论的分析能力，在图像分析和可以为功能数据带来类似的收益。该提案的主要目标是开发新的空间推理方法，直接解决估计非稀疏信号并量化其空间不确定性，以增加统计数据功率、控制错误率并获得适当的可解释结果。在本次资助的上一个周期中，我们建立了峰检测中形式误差控制的方法。此次更新开发地点峰的不确定性和检测能力（目标 1），并进一步开发置信带和整个信号（目标 2）和信号超过的偏移集的空间置信区域阈值（目标 3）。提出了针对平均值（效应幅度）和信噪比的方法比率（标准化平均值或效应大小），允许在空间非存在的情况下进行可解释的推断恒定方差，神经影像数据的特征。该提案提供了明确的空间定义推理，并支持平滑高斯随机场理论的方法，放弃平稳性和零均值假设。这些方法经过严格验证并用于绘制认知效果图美国国立卫生研究院 (NIH) 资助的大型青少年大脑认知发展 (ABCD) 研究中的成瘾物质使用情况。该提案独特地汇集了图像和功能数据分析的想法，以提供更多平滑信号和图像中重要影响的准确且可解释的空间定位。这该提案中开发的方法提供了更准确的大脑和其他领域的映射，并且更高的统计能力来确定发生重要影响的地点，增强科学性了解并指导更有针对性的后续研究。