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

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).

摘要 糖尿病相关和代谢性疾病，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世界性流行病。从全球的角度来看， 今天，全世界有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。 所有.代谢性疾病，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机会，研究的组成和形态， 以一种以前不可能的方式进入人体。在体内进行的当代成像研究 对大量参与者的研究使得对年龄相关和 代谢疾病和药物干预的影响。先进成像技术的出现 技术的发展也产生了对用于识别和 解剖结构和组织的形态学模式的量化及其随年龄增长的变化。 该项目将为研究人类提供新颖的非侵入性医学图像分析技术 身体组成，以实现这些病理的及时预后。我们的研究兴趣将集中在 确定大腿中部、腹部和小腿的形态模式，最终导致 成像生物标志物的发展。脂肪组织在人体内的积累及其变化 区域分布与2型糖尿病、心血管疾病和代谢综合征有关。 骨骼肌组成中的骨骼肌相关变化与肌肉力量和质量的损失密切相关， 通常称为肌肉减少症，导致活动性和功能下降。此外，骨小梁结构 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 老龄化纵向研究（BLSA）是美国持续时间最长的流行病学研究。 这项工作将解决一个技术和临床假设。技术假设是， 图像分析可以准确且鲁棒地分割、配准和融合通过以下方式获取的身体组成数据： 现代MRI和CT成像扫描仪。临床假设是，定性身体组成表型 可作为代谢综合征预后和诊断的生物标志物 表现。我们将在医学图像分析的最新进展的基础上， 研究人体成分及其纵向变化的技术，主要应用于组织 在大腿中部、小腿和腹部进行识别和定量（目标1）。然后我们将开发 统计机器学习方法，以实现代谢和年龄相关的及时诊断和预后 包括代谢综合征和骨质疏松症在内的疾病，并跟踪干预措施的效果（目标2）。

项目成果

Bone texture characterization for osteoporosis diagnosis using digital radiography.

• DOI: 10.1109/embc.2016.7590879
• 发表时间: 2016-08
• 期刊: Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
• 作者: Keni Zheng;Makrogiannis S
• 通讯作者: Makrogiannis S

Multi-atlas segmentation and quantification of muscle, bone and subcutaneous adipose tissue in the lower leg using peripheral quantitative computed tomography.

• DOI: 10.3389/fphys.2022.951368
• 发表时间: 2022
• 期刊: FRONTIERS IN PHYSIOLOGY
• 影响因子: 4
• 作者: Makrogiannis, Sokratis;Okorie, Azubuike;Di Iorio, Angelo;Bandinelli, Stefania;Ferrucci, Luigi
• 通讯作者: Ferrucci, Luigi

Discriminative Localized Sparse Approximations for Mass Characterization in Mammograms.

• DOI: 10.3389/fonc.2021.725320
• 发表时间: 2021
• 期刊: Frontiers in oncology
• 影响因子: 4.7
• 作者: Makrogiannis S;Zheng K;Harris C
• 通讯作者: Harris C

Automated skeletal tissue quantification in the lower leg using peripheral quantitative computed tomography.

• DOI: 10.1088/1361-6579/aaafb5
• 发表时间: 2018-04-03
• 期刊: Physiological measurement
• 影响因子: 3.2
• 作者: Makrogiannis S;Boukari F;Ferrucci L
• 通讯作者: Ferrucci L

Spatially localized sparse representations for breast lesion characterization.

用于乳腺病变表征的空间局部稀疏表示。

• DOI: 10.1016/j.compbiomed.2020.103914
• 发表时间: 2020
• 期刊: Computers in biology and medicine
• 影响因子: 7.7
• 作者: Zheng,Keni;Harris,Chelsea;Bakic,Predrag;Makrogiannis,Sokratis
• 通讯作者: Makrogiannis,Sokratis