Modelling and Matching 3D Objects for Medical Image Analysis

点击翻译按钮获取中文摘要

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.