Automating the Analysis of Aortic Calcifications in DXA Images

自动分析 DXA 图像中的主动脉钙化

• 批准号: 1790130
• 金额: --
• 依托单位: University of Manchester
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2016
• 资助国家: 英国
• 起止时间: 2016 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-1790130
• 关键词: Automating; Analysis; Aortic; Calcifications; DXA

Abdominal aortic calcifications (AAC) are build ups of calcium, fat and other material accumulated in the walls of the aorta. Their location and severity have been shown to be an important predictor of death through vascular disease, myocardial infarction and stroke thus their detection in a patient is important for diagnosing problems with the cardiovascular system. It has been shown that AACs can be visible on low dose DXA images of the spine. Large numbers of DXA images of the spine are taken at the Manchester Royal Infirmary for identifying vertebral fractures. The presents an opportunity for early detection of cardio-vascular disease at the same time as vertebral screening. Current scoring systems for measuring AACs are time-consuming for radiologists to perform.In this project we propose to develop a computer system capable of automatically detecting signs of AACs on DXA images, with minimal human intervention, in order to speed up the analysis of such images.Objectives:To write a tool to allow manual annotation of AAC regions in DXA imagesTo build a large annotated set of DXA images of the spine suitable for training and testingTo develop both semi- and fully-automatic systems for identifying AACs in DXA imagesTo evaluate the performance of the systems against standard radiologist scoring schemesThe project will build on the Centre's world-class algorithms for accurately locating the vertebrae in images of the spine to identify the regions of the image likely to contain the aorta. Machine learning techniques will be used to develop classifiers to determine regions associated with AACs.The ability to automatically detect AACs would be of great benefit in early diagnosis of cardio-vascular disease, particularly as it would allow opportunistic application to existing images, potentially identifying the disease on people who might not otherwise be examined.

腹主动脉钙化（AAC）是钙、脂肪和其他物质积聚在主动脉壁中。它们的位置和严重程度已被证明是通过血管疾病、心肌梗死和中风死亡的重要预测因素，因此它们在患者中的检测对于诊断心血管系统的问题是重要的。已经表明，AAC可以在脊柱的低剂量DXA图像上可见。在曼彻斯特皇家医院拍摄了大量的脊柱DXA图像，用于识别椎骨骨折。这为在脊椎筛查的同时早期发现心血管疾病提供了机会。目前用于测量AAC的评分系统对于放射科医生来说是耗时的。在本项目中，我们建议开发一种能够自动检测DXA图像上的AAC迹象的计算机系统，以最小的人为干预，以加快此类图像的分析。目的：编写一个工具，允许在DXA图像中手动注释AAC区域构建一个适合训练和测试的脊柱DXA图像的大型注释集开发半和全在DXA图像中识别主动脉弓的自动系统为了根据标准放射科医生评分方案评估系统的性能，该项目将建立在该中心的世界级算法基础上，用于准确定位脊柱图像中的椎骨，以识别图像中可能包含主动脉弓的区域。机器学习技术将被用于开发分类器，以确定与AAC相关的区域。自动检测AAC的能力将在心血管疾病的早期诊断中受益匪浅，特别是因为它将允许对现有图像进行机会性应用，从而有可能识别出可能无法进行检查的人的疾病。