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

摘要 与年龄有关的代谢性疾病，如2型糖尿病、心血管疾病和骨质疏松症 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，大致 今天，世界上有3.438亿人患有2型糖尿病，1.75亿人不知道自己患有糖尿病 全。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。 现代医学成像技术为研究细胞的组成和形态计量提供了机会 以一种以前不可能实现的方式。在活体内进行的当代成像研究 对大量参与者进行了与年龄相关的横断面和纵向研究 代谢性疾病，以及药物干预的效果。先进成像技术的出现 技术也创造了对自动图像分析技术的需求，以识别和 对解剖和组织的形态模式及其随年龄增长的变化进行量化。 本项目将为人类研究提供新的、无创的医学图像分析技术。 身体成分以实现对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，最终将导致 成像生物标记物的发展。脂肪组织在人体内的蓄积及其变化 地区分布与2型糖尿病、心血管疾病和代谢综合征有关。 骨骼肌成分与年龄相关的变化与肌肉力量和质量的丧失密切相关， 常被称为骨质疏松症，导致活动能力和功能下降。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说。技术假设是量化的 图像分析可以准确而稳健地分割、配准和融合人体成分数据 现代核磁共振和CT成像扫描仪。临床假说是定性的身体成分表型 临床影像可作为代谢综合征预后和诊断的生物标志物 表现形式。我们将在医学图像分析的最新进展的基础上，做出新的和非侵入性的贡献 人体成分及其纵向变化研究技术及其在组织中的主要应用 在大腿中部、小腿和腹部进行识别和量化(目标1)。然后我们就会发展 统计机器学习方法，实现对代谢和年龄相关疾病的及时诊断和预后 评估包括代谢综合征和骨质疏松症在内的各种情况，并跟踪干预措施的效果(目标2)。

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).