Image Analysis and Machine Learning Methods for Biomarkers of Age-related and Metabolic Diseases

年龄相关疾病和代谢疾病生物标志物的图像分析和机器学习方法

• 批准号: 10663229
• 负责人: Sokratis Makrogiannis
• 金额: $ 10.95万
• 依托单位: DELAWARE STATE UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2015
• 资助国家: 美国
• 起止时间: 2015-04-02 至 2025-07-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10663229
• 关键词: Abdomen; Adipose tissue; Adult; Affect; Age; Aging; Agreement; Anatomy; Area; Atlases; Baltimore; Biological Markers; Body Composition; Body Regions; Bone structure; Cardiovascular Diseases; Classification; Clinical; Clinical Data; Computational Technique; Computer Assisted; Coronary Arteriosclerosis; Data; Data Set; Descriptor; Deterioration; Development; Diabetes Mellitus; Diagnosis; Diagnostic; Dictionary; Disease; Doctor of Medicine; Doctor of Philosophy; Endocrinology; Epidemic; Epidemiology; Gerontology; Human; Human body; Image; Image Analysis; Imaging technology; Intervention; Leg; Link; Longitudinal Studies; Magnetic Resonance Imaging; Measures; Medical; Medical Imaging; Metabolic; Metabolic Diseases; Metabolic syndrome; Methods; Modernization; Monitor; Morphologic artifacts; Morphology; Muscle; Noise; Non-Insulin-Dependent Diabetes Mellitus; Normal tissue morphology; Obesity; Organ; Osteoporosis; Participant; Pathology; Pattern; Pennsylvania; Persons; Phenotype; Physics; Physiological; Population Study; Prevalence; Process; Prognosis; Property; Quality of life; Research; Risk; Risk Factors; Shapes; Skeletal Muscle; Stroke; Techniques; Testing; Therapeutic Intervention; Thigh structure; Tissue Model; Tissues; United States; United States National Institutes of Health; Visualization; Work; X-Ray Computed Tomography; age related; automated image analysis; clinical imaging; deep learning; disease diagnostic; disorder risk; epidemiology study; fracture risk; imaging biomarker; imaging modality; imaging study; in vivo; interest; intervention effect; learning strategy; longitudinal analysis; machine learning method; mathematical methods; medical schools; metabolic abnormality assessment; models and simulation; morphometry; muscle form; muscle strength; novel; pandemic disease; pharmacologic; pre-clinical; prognosis biomarker; quantitative imaging; radiological imaging; sarcopenia; simulation; statistical and machine learning; statistical learning; substantia spongiosa; ultrasound; virtual; Abdomen; Adipose tissue; Adult; Affect; Age; Aging; Agreement; Anatomy; Area; Atlases; Baltimore; Biological Markers; Body Composition; Body Regions; Bone structure; Cardiovascular Diseases; Classification; Clinical; Clinical Data; Computational Technique; Computer Assisted; Coronary Arteriosclerosis; Data; Data Set; Descriptor; Deterioration; Development; Diabetes Mellitus; Diagnosis; Diagnostic; Dictionary; Disease; Doctor of Medicine; Doctor of Philosophy; Endocrinology; Epidemic; Epidemiology; Gerontology; Human; Human body; Image; Image Analysis; Imaging technology; Intervention; Leg; Link; Longitudinal Studies; Magnetic Resonance Imaging; Measures; Medical; Medical Imaging; Metabolic; Metabolic Diseases; Metabolic syndrome; Methods; Modernization; Monitor; Morphologic artifacts; Morphology; Muscle; Noise; Non-Insulin-Dependent Diabetes Mellitus; Normal tissue morphology; Obesity; Organ; Osteoporosis; Participant; Pathology; Pattern; Pennsylvania; Persons; Phenotype; Physics; Physiological; Population Study; Prevalence; Process; Prognosis; Property; Quality of life; Research; Risk; Risk Factors; Shapes; Skeletal Muscle; Stroke; Techniques; Testing; Therapeutic Intervention; Thigh structure; Tissue Model; Tissues; United States; United States National Institutes of Health; Visualization; Work; X-Ray Computed Tomography; age related; automated image analysis; clinical imaging; deep learning; disease diagnostic; disorder risk; epidemiology study; fracture risk; imaging biomarker; imaging modality; imaging study; in vivo; interest; intervention effect; learning strategy; longitudinal analysis; machine learning method; mathematical methods; medical schools; metabolic abnormality assessment; models and simulation; morphometry; muscle form; muscle strength; novel; pandemic disease; pharmacologic; pre-clinical; prognosis biomarker; quantitative imaging; radiological imaging; sarcopenia; simulation; statistical and machine learning; statistical learning; substantia spongiosa; ultrasound; virtual

Abstract Age-related and metabolic diseases such as type-2 diabetes, cardiovascular diseases, and sarcopenia have become a worldwide epidemic that affects the quality of life of millions. To give a global perspective, roughly 343.8 million people in the world have type-2 diabetes today, and 175 million do not know they have diabetes at all. Metabolic diseases, such as diabetes and osteoporosis, are strongly linked to longitudinal changes in body composition, morphology and function. Modern medical imaging technologies offer the opportunity to study the composition and morphometry of human body in ways that were previously impossible. Contemporary imaging studies that are performed in vivo on a large number of participants have enabled cross-sectional and longitudinal studies of age-related and metabolic diseases, and effects of pharmacological interventions. The emergence of advanced imaging technologies has also created the need for automated image analysis techniques for identification and quantification of morphological patterns of anatomies and tissues and their changes with increasing age. This project will contribute novel and non-invasive medical image analysis techniques for studying the human body composition to achieve timely prognosis of these pathologies. Our research interests will concentrate on identification of morphological patterns in the mid-thigh, abdomen and lower leg that will eventually lead to development of imaging biomarkers. The accumulation of adipose tissue in the human body and changes of its regional distribution are associated with type-2 diabetes, cardiovascular diseases and the metabolic syndrome. Age-related changes in skeletal muscle composition are strongly linked to loss in muscle strength and mass, frequently termed as sarcopenia, leading to decreased mobility and function. Also, trabecular bone structural changes are associated with osteoporosis. We will use imaging and clinical data collected by the Baltimore Longitudinal Study of Aging (BLSA) that is the longest ongoing epidemiology study in the US. This work will address a technical and a clinical hypothesis. The technical hypothesis is that quantitative image analysis can accurately and robustly segment, register and fuse body composition data acquired by modern MRI and CT imaging scanners. The clinical hypothesis is that qualitative body composition phenotypes on clinical imaging can be used as biomarkers for prognosis and diagnosis of the metabolic syndrome manifestations. We will build on recent advances in medical image analysis to contribute novel and non-invasive techniques for studying the human body composition and its longitudinal changes with main applications in tissue identification and quantification at the mid-thigh, lower leg and the abdomen (aim 1). Then we will develop statistical machine learning methods to achieve timely diagnosis and prognosis of metabolic and age-related conditions including the metabolic syndrome and osteoporosis, and to track the effect of interventions (aim 2).

抽象的 与年龄相关的和代谢性疾病，例如2型糖尿病，心血管疾病和肌肉减少症 成为影响数百万人生活质量的全球流行病。给出全球视角，粗略 当今世界上有3.438亿人患有2型糖尿病，1.75亿人不知道他们患有糖尿病 全部。代谢疾病，例如糖尿病和骨质疏松症，与身体的纵向变化密切相关 组成，形态和功能。 现代医学成像技术提供了研究的机会来研究 人体以以前不可能的方式。在体内进行的现代成像研究 在大量参与者中，已经实现了与年龄有关的横断面和纵向研究 代谢疾病和药理学干预的影响。高级成像的出现 Technologies还创造了对自动图像分析技术的需求 量化解剖学和组织的形态模式及其随着年龄的增长而变化。 该项目将为研究人类提供新颖和非侵入性医学图像分析技术 身体组成以及时预后这些病理。我们的研究兴趣将集中在 大腿中部，腹部和下腿中形态学模式的识别，最终会导致 成像生物标志物的开发。脂肪组织在人体中的积累及其变化 区域分布与2型糖尿病，心血管疾病和代谢综合征有关。 与年龄相关的骨骼肌组成的变化与肌肉强度和质量的损失密切相关， 经常被称为肌肉减少症，导致迁移率和功能降低。另外，小梁骨结构 变化与骨质疏松症有关。我们将使用巴尔的摩收集的成像和临床数据 衰老的纵向研究（BLSA）是美国正在进行的流行病学研究最长的研究。 这项工作将解决技术和临床假设。技术假设是定量 图像分析可以准确，稳健地细分，注册和融合身体组成数据 现代MRI和CT成像扫描仪。临床假设是定性身体组成表型 临床成像可以用作代谢综合征预后和诊断的生物标志物 表现。我们将基于医学图像分析的最新进展，以贡献新颖和无创的 研究人体组成及其在组织中主要应用的纵向变化的技术 大腿中部，下腿和腹部的识别和定量（AIM 1）。然后我们将发展 统计机器学习方法，以及时诊断和预后代谢和年龄有关 包括代谢综合征和骨质疏松症，以及跟踪干预措施的影响（AIM 2）。