Spatial inference methods for image analysis

图像分析的空间推理方法

• 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
• 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

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，图像分析中的一个核心问题是识别和 找到重要的效果。原型示例是3D脑体积或2D皮质的图像分析 表面，使用解剖和功能成像。示例在1D功能数据中也比比皆是（脑电图 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 问题共享一种常见的数据结构，其中反复观察到平稳的功能或图像，并且 在细网格上对齐。本地化的目的是确定信号强的区域或 条件或受试者组之间存在差异。 虽然有大量的工具来分析成像数据，但重点主要集中在 零假设下的显着性测试和控制错误率，并受到实际限制 但是关于噪声场，损害误差控制和统计能力的不切实际的假设。在 另一方面，功能数据分析方法正确地在非零均值模型下工作，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直接解决该方法的新的空间推理方法 为了增加统计的估计，估计非SPARSE信号和空间不确定性的量化 功率，控制错误率并获得适当解释的结果。在这笔赠款的上一个周期中， 我们在峰值检测中建立了正式误差控制的方法。此更新开发位置 峰值的不确定性和检测能力（AIM 1），并进一步发展以发展信心带和 整个信号的空间置信区域（AIM 2）以及信号超过A的游览集 阈值（目标3）。提出方法是针对平均值（效应幅度）和信噪比的方法 比率（标准均值或效果大小），可以在存在空间非 - 恒定方差，神经影像数据的特征。该提案提供了空间的明确定义 推理，并通过平滑的高斯随机场理论支持该方法，放弃了平稳性 和零均值的假设。这些方法经过严格验证，并用于绘制认知效果 大型NIH资助的青少年脑认知发展（ABCD）研究中的成瘾性药物使用。 该建议独特地从图像和功能数据分析中汇集了想法，以提供更多 在平滑信号和图像中重要效应的准确和可解释的空间定位。这 该提案中开发的方法提供了更准确的大脑和其他域的映射，以及 更高的统计能力确定发生重要影响的位置，增强科学 了解和指导更好的目标后续研究。