Automated quantification of lipid droplets in fatty liver tissue sections

脂肪肝组织切片中脂滴的自动定量

• 批准号: 7539764
• 负责人: PATRICK M MCDONOUGH
• 金额: $ 29.85万
• 依托单位: VALA SCIENCES, INC.
• 依托单位国家: 美国
• 财政年份: 2008
• 资助国家: 美国
• 起止时间: 2008-07-01 至 2010-06-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/7539764
• 关键词: Affect; Alcoholic Liver Diseases; Alcoholism; Algorithms; American; Animal Model; Apoptosis; Area; Biomedical Engineering; Biopsy; Biopsy Specimen; Categories; Cell Death; Cell Density; Cell Nucleus; Cells; Child; Cirrhosis; Classification; Classification Scheme; Clinical Research; Clinical Trials; Collaborations; Computer software; Condition; Cooperative Human Tissue Network; Cultured Cells; Custom; Databases; Development; Diabetes Mellitus; Disease; Failure; Family suidae; Fatty Liver; Fatty acid glycerol esters; Fibrosis; Frequencies; General Population; Goals; Grouping; Health; Hematoxylin and Eosin Staining Method; Hepatitis C; Hepatitis C virus; Hepatocyte; Human; Image; Image Analysis; Image Cytometry; Incidence; Infiltration; Inflammation; Institutes; Internet; Judgment; Lipid Inclusion; Lipids; Liver; Liver Dysfunction; Liver diseases; Microscope; Microscopy; Necrosis; Nuclear; Numbers; Numeric Rating Scale; Obesity; Optics; Overweight; Pathologist; Pathology; Patients; Phase; Population; Price; Protocols documentation; Public Health; Rate; Reagent; Registries; Research; Research Personnel; Rodent; Sampling; Scanning; Science; Score; Services; Slide; Small Business Technology Transfer Research; Societies; Software Engineering; Staining method; Stains; Steatohepatitis; Sus scrofa; System; Techniques; Testing; Therapeutic; Therapeutic Effect; Tissue Sample; Tissues; Training; Upper arm; Virus Diseases; authority; digital; digital imaging; image processing; improved; interest; light transmission; liver biopsy; liver imaging; macrophage; non-alcoholic fatty liver; nonalcoholic steatohepatitis; programs; prototype; rapid technique; research and development; research study; size; tool

DESCRIPTION (provided by applicant): Non-alcoholic fatty liver disease (NAFLD) is emerging as a health crisis and is caused by the accumulation of lipid within liver cells (hepatocytes). The disease is similar to alcoholic liver disease, as lipid droplets (steatosis) appear first, followed by damage to the hepatocytes (steatohepatitis), cell death, inflammation, and cirrhosis. Alarmingly, the incidence of NAFLD in the US may be as high as 30% of the general population, and occurs at even higher incidence in patients suffering from obesity, diabetes, and hepatitis C viral infection, indicating that there will soon be a dramatic increase in the rates of liver dysfunction and failure. Researchers world-wide are studying fatty liver in human patients and in a variety of animal models, using semi-quantitative scoring techniques to estimate the degree of hepatic steatosis. The goal of this Phase I STTR project is to develop image-analysis techniques to quantify the grade of steatosis, percentage of fat, and the frequency and size of lipid droplets in images obtained from liver biopsies and tissue sections. The proposed algorithm will be particularly useful in recognizing subtle changes in steatosis which are likely to occur in studies utilizing experimental therapeutics. The algorithm will be developed with images obtained from human liver biopsies by Dr. Zachary Goodman, a leading authority on fatty liver and will provide a new research tool for quantifying steatosis that will be of high interest to researchers in this critical area of human health. PUBLIC HEALTH RELEVANCE: "Fatty liver disease" is a condition that affects a high proportion of the US population, particularly people that are overweight or obese. Fatty liver is characterized by the occurrence of fat droplets within the cells that make up the liver. Tissue samples (biopsies) are commonly taken from livers as part of the protocol for research studies on fatty liver disease, and the fat content of the samples are graded by pathologists, using microscopes and somewhat grading criteria, that is, at best, semi-quantitative. The proposed research will develop a technique for rapid and precise automatic quantitation of the fat content of liver biopsies, which will be an aid to workers investigating fatty liver disease.

描述（由申请人提供）：非酒精性脂肪性肝病（NAFLD）正在成为一种健康危机，是由肝细胞（肝细胞）内脂质蓄积引起的。这种疾病类似于酒精性肝病，首先出现脂滴（脂肪变性），然后是肝细胞损伤（脂肪性肝炎），细胞死亡，炎症和肝硬化。令人担忧的是，在美国，NAFLD的发病率可能高达一般人群的30%，并且在患有肥胖症、糖尿病和丙型肝炎病毒感染的患者中发生率甚至更高，这表明肝功能障碍和衰竭的发生率将很快急剧增加。世界各地的研究人员正在研究人类患者和各种动物模型中的脂肪肝，使用半定量评分技术来估计肝脏脂肪变性的程度。该一期STTR项目的目标是开发图像分析技术，以量化从肝活检和组织切片获得的图像中脂肪变性的等级、脂肪百分比以及脂滴的频率和大小。所提出的算法将是特别有用的识别脂肪变性的细微变化，这是可能发生在利用实验疗法的研究。该算法将使用脂肪肝领域的权威Zachary Goodman博士从人类肝脏活检中获得的图像进行开发，并将为量化脂肪变性提供一种新的研究工具，这将引起人类健康这一关键领域研究人员的高度兴趣。公共卫生相关性： “脂肪肝”是一种影响美国人口比例很高的疾病，特别是超重或肥胖的人。脂肪肝的特点是在组成肝脏的细胞内出现脂肪滴。组织样本（活检）通常取自肝脏，作为脂肪肝研究方案的一部分，病理学家使用显微镜和某种分级标准对样本的脂肪含量进行分级，也就是说，充其量是半定量的。这项拟议的研究将开发一种快速精确自动定量肝活检脂肪含量的技术，这将有助于调查脂肪肝疾病的工作人员。