Modelling and Matching 3D Objects for Medical Image Analysis

近年来，医学领域产生的体积图像的数量有了巨大的增长。PET、MR和CT机器正变得越来越常见，它们是研究人体解剖和功能的必要工具。然而，解释3D图像是困难的。尽管放射科医生非常擅长做出定性判断，但要做出准确的定量测量，需要仔细地注释3D体积中感兴趣的结构，这是很难实现的。因此，人们进行了广泛的研究，以开发软件工具来帮助从这些数据中提取有用的信息。这些方法从简单的图像标注工具(帮助手动分割)到全自动系统(理想情况下)不需要人工干预。尽管已经取得了重大进展，但在自动系统能够产生真正可靠的结果并适合广泛应用之前，仍然有许多困难的问题必须解决。这个项目试图解决这些问题。我们的团队开创了许多方法来构建人体内部结构形状和外观的统计模型。它们描述了这些结构在健康和疾病受试者中的形状变化方式，并可以快速与新图像匹配，使人们能够确定该图像中结构的形状。这些方法已经被世界各地的研究人员和公司采用，并产生了很大的影响。然而，目前将它们应用到新问题上需要大量的时间和技能。该项目旨在开发方法，使模型在新领域的使用变得更加简单，并产生更准确和更稳健的结果。新方法将通过在三个不同的结构-膝盖、肾脏和大脑器官上使用来进行评估。预计这些方法将使当前基于模型的技术得到更广泛的采用，并将对临床和药物研究产生影响。

Over recent years there has been a huge increase in the number of volumetricimages generated in the medical domain. PET, MR and CT machines are becomingmore common, and are essential tools to investigate the anatomy and functionof the body. However, interpretting 3D images is difficult. Althoughradiologists are very good at making qualitative judgements, to makeaccurate quantitative measurements requires careful annotation ofthe structures of interest in a 3D volume, which is hard to achieve.There has thus been an extensive research effort to develop software toolsto help extract useful information from such data. These range fromsimple image annotation tools (to aid manual segmentation) to fullyautomatic systems which (ideally) require no manual intervention.Although significant progress has been made, there are still manydifficult problems which must be tackled before automatic systems canproduce really reliable results and are suitable for wide application.This project seeks to address such issues.Our group has pioneered a number of methods of constructing statistical models of the shape and appearance of structures within the body. These describe the way such structures vary in shape across healthy and diseased subjects, and can be rapidly matched to new images, allowing one to determine the shape of the structure in that image. These methods have been adopted by both researchers and companies world-wide, and have been very influential. However, it currently takes significant time and skill to apply them to new problems. This project aims to develop methods which will make it much simpler to use the models in new areas, and will produce more accurate and robust results.The new methods will be evaluated by using them on three different structures - knees, kidneys and organs within the brain. It is anticipated that the methods will allow more widespread adoption of the current model-based techniques, and will have an impact in both clinical and pharmaceutical research.