Enhanced Software Tools for Detecting Anatomical Differences in Image Data Sets

用于检测图像数据集中的解剖差异的增强软件工具

基本信息

批准号：
10115288
负责人：
Samuel Gerber
金额：
$ 9.99万
依托单位：
KITWARE, INC.
依托单位国家：
美国
项目类别：
财政年份：
2018
资助国家：
美国
起止时间：
2018-09-18 至 2020-08-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/10115288
关键词：
Algorithmic Software Algorithms Alzheimer&apos s Disease Anatomy Brain Calibration Clinical Clinical Research Cluster Analysis Computer software Data Set Databases Dementia Deterioration Development Diffuse Disease Drug Screening Early Diagnosis Foundations Goals Grain Image Image Analysis Internet Lead Location Machine Learning Medical Imaging Methodology Methods Modality Nature Nerve Degeneration Neurologic Neurologic Effect Online Systems Outcome Pharmaceutical Preparations Pharmacotherapy Phase Population Study Positioning Attribute Positron-Emission Tomography Process Research Research Personnel Services Shapes Software Tools Structure Technology Temporal Lobe Testing Validation Variant Visualization autism spectrum disorder base clinical Diagnosis experience frontal lobe gray matter high throughput screening image processing image registration imaging capabilities improved interest machine learning method morphometry nervous system disorder predict clinical outcome predictive modeling programs research and development shape analysis software development task analysis tool web services white matter

项目摘要

Project Summary Morphometric analysis is a primary algorithmic tool to discover disease and drug related effects on brain anatomy. Neurological degeneration and disease manifest in subtle and varied changes in brain anatomy that can be non-local in nature and effect amounts of white and gray matter as well as relative positioning and shapes of local brain anatomy. State-of-the-art morphometry methods focus on local matter distribution or on shape variations of apriori selected anatomies but have difficulty in detecting global or regional deterioration of matter; an important effect in many neurodegenerative processes. The proposal team recently developed a morphometric analysis based on unbalanced optimal transport, called UTM, that promises to be capable to discover local and global alteration of matter without the need to apriori select an anatomical region of interest. The goal of this proposal is to develop the UTM technology into a software tool for automated high-throughput screening of large neurological image data sets. A more sensitive automated morphometric analysis tool will help researchers to discover neurological effects related to disease and lead to more efficient screening for drug related effects.

项目摘要形态分析是发现疾病和药物相关影响大脑的主要算法工具解剖学。神经变性和疾病在脑解剖学的细微变化中表现出来，在本质上可能是非本地的，白色和灰色物质以及相对定位的效果量以及局部脑解剖结构的形状。最先进的形态计量学方法的重点是本地物质分布或 APRIORI的形状变化选择了解剖学，但难以检测全球或区域恶化事情;在许多神经退行性过程中的重要效果。提案团队最近开发了基于不平衡的最佳运输（称为UTM）的形态分析有望能够发现物质的局部和全球变化，而无需选择一个感兴趣的解剖区域。该提案的目的是将UTM技术开发成自动化的软件工具大型神经图像数据集的高通量筛选。更敏感的自动化形态分析工具将帮助研究人员发现与疾病有关的神经系统作用，并导致对药物相关的效果进行更有效的筛查。