Automatic segmentation and motion analysis of the knee joint based on MRI and 4DCT images

Lower limbs disorders, such as osteoarthritis (OA), knee bursitis, meniscal lesions, are musculoskeletal diseases that affect the hips, knees and legs, and usually lead to chronic pain conditions and limited mobility. Knee joint pains can interfere with daily activities, from sports to climbing stairs to walking. In this sense, a relatively minor knee defect can have a great impact on everyday life and create negative conditions for further health deterioration. In the past decades, rapid advances in medical imaging and increased understanding of musculoskeletal mechanics, and as a result diagnosis and effective treatment of knee joint disorders have improved substantially. Nevertheless, the available tools for researchers, radiologists and orthopaedic surgeons to assess the biomechanical condition of patients still lack in delivering a quantitative analysis of muscle and ligament ruptures, cartilage deterioration and patella misalignment, forcing the clinicians to rely mostly on their own subjective judgement which can lead sub-optimal care. One of the main bottlenecks in delivering quantitative digital models of the knee joint is the fast and accurate segmentation of the various knee structures from medical images. Manual segmentation is intrinsically subjective and time-consuming despite the developments in software, and accurate segmentation requires both clinical and user interface experience. As a part of the ERC BioMechTools project, the work described in this thesis is dedicated to advancing existing methods and developing new methods for robust, accurate and maximally automated segmentation and clinical measure determination relevant to pathology assessment in the knee joint.