Automating the Analysis of Aortic Calcifications in DXA Images

腹主动脉钙化（AAC）是由钙、脂肪和其他物质在主动脉壁上堆积而成。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，因此在患者中检测它们对于诊断心血管系统问题非常重要。已有研究表明，在脊柱的低剂量DXA图像上可以看到AACs。在曼彻斯特皇家医院拍摄了大量脊柱的DXA图像，用于识别椎体骨折。在椎体筛查的同时，这为早期发现心血管疾病提供了机会。目前用于测量AACs的评分系统对放射科医生来说非常耗时。在本项目中，我们建议开发一种能够自动检测DXA图像上AACs迹象的计算机系统，以减少人为干预，以加快对此类图像的分析。目的：编写一个工具，允许手动标注DXA图像中的AAC区域；构建适合训练和测试的大型脊柱DXA图像注释集；开发半自动和全自动系统，用于识别DXA图像中的AAC；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的基础上，用于准确定位脊柱图像中的椎骨，以识别脊柱的区域图像可能包含主动脉。机器学习技术将用于开发分类器，以确定与aac相关的区域。自动检测AACs的能力将对心血管疾病的早期诊断有很大的好处，特别是因为它允许对现有图像进行机会性应用，有可能在那些可能不被检查的人身上识别疾病。

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.