Automatic Segmentation of Skeleton in Whole-Body MR Images

Abstract: Magnetic Resonance Imaging(MRI) has developed as a widespread technique to examine various body parts and diagnose a wide range of diseases. MRI can often be superior to other imaging techniques such as Computed Tomography(CT) since it does not use ionizing radiation and can give a clearer image of soft tissue. As MRI becomes a more important part in medicine the demands on software to analyse the images and extract useful information increases. Today medical image analysis can be used to localise tumours, measure brain substance and to isolate specific organs. Although much has happened in the field in recent years there is still little published about segmentation of skeleton in MRI images, this might be because cortical bonen either contains fat nor water and thus gives a weak signal in MRI images. Skeletal segmentation could still be useful to localise other body parts, to guide further analysis of whole body images and to do attenuation correction in PET/MRI systems. This work aims to increase the knowledge about skeletal segmentation in fat and water(FWI) MR images, and the goal is to produce a method that is flexible and robust enough to work on different MR machines with patients of various body types. This work implemented and evaluated two methods for skeletal segmentation in fat and water MR images. The first method divided the body into different regions and segmented each region with a region-specific algorithm and the other method consisted of a filter that detect patterns in the proximity of bone.The evaluation used reference segmentations performed with the program SmartPaint, and overlap with the automatic method was measured. Subjects used in this work originated from two studies, one on small patients and one on larger patients, thus giving an indication of how well the methods work on a population with large variance. Results show that the filter method produce a more accurate result than the body division method. The body division method had an average dice coefficient of 0.836, over segmentation ratio of 0.225 and under segmentation ratio of 0.120. The filter method had a dice coefficient of 0.944 and over and under segmentation rates were both 0.055. Both methods needed post processing in order to get a result that minimised the over segmentation in order to achieve an acceptable result. Neither of the methods allows accurate assessment of bone volume, but an approximation might be possible with the filter method. This project has shown that it is possible to segment skeleton in whole body MRimages with a decent result without using either registration or deformable models. More advanced methods will most likely be needed to minimise the over segmentation and increase segmentation accuracy.