Fuzzy Hybrid Method for the Reconstruction of 3D Models Based on CT/MRI Data

This research proposes a hybrid method for improving the segmentation accuracy of reconstructed 3D models from computed tomography/magnetic resonance imaging (CT/MRI) data. A semi-automatic hybrid method based on combination of Fuzzy C-Means clustering (FCM) and region growing (RG) is proposed. In this approach, FCM is used in the first stage as a preprocessing step in order to classify and improve images by assigning pixels to the clusters for which they have the maximum membership, and manual selection of the membership intensity map with the best contrast separation. Afterwards, automatic seed selection is performed for RG, for which a new parameter standard deviation (STD) of pixel intensities, is included. It is based on the selection of an initial seed inside a region with maximum value of STD. To evaluate the performance of the proposed method, it was compared to several other segmentation methods. Experimental results show that the proposed method overall provides better results compared to other methods in terms of accuracy. The average sensitivity and accuracy rates for cone-beam computed tomography CBCT 1 and CBCT 2 datasets are 99 %, 98.4 %, 47.2 % and 89.9 %, respectively. For MRI 1 and MRI 2 datasets, the average sensitivity and accuracy values are 99.1 %, 100 %, 75.6 % and 99.6 %, respectively. The average values for the Dice coefficient and Jaccard index for the CBCT 1 and CBCT 2 datasets are 95.88, 0.88, 0.6, and 0.51, respectively, while for MRI 1 and MRI 2 datasets, average values are 0.96, 0.93, 0.81 and 0.7, respectively, which confirms the high accuracy of the proposed method

Fuzzy Hybrid Method for the Reconstruction of 3D Models Based on CT/MRI Data

This research proposes a hybrid method for improving the segmentation accuracy of reconstructed 3D models from computed tomography/magnetic resonance imaging (CT/MRI) data. A semi-automatic hybrid method based on combination of Fuzzy C-Means clustering (FCM) and region growing (RG) is proposed. In this approach, FCM is used in the first stage as a preprocessing step in order to classify and improve images by assigning pixels to the clusters for which they have the maximum membership, and manual selection of the membership intensity map with the best contrast separation. Afterwards, automatic seed selection is performed for RG, for which a new parameter standard deviation (STD) of pixel intensities, is included. It is based on the selection of an initial seed inside a region with maximum value of STD. To evaluate the performance of the proposed method, it was compared to several other segmentation methods. Experimental results show that the proposed method overall provides better results compared to other methods in terms of accuracy. The average sensitivity and accuracy rates for cone-beam computed tomography CBCT 1 and CBCT 2 datasets are 99 %, 98.4 %, 47.2 % and 89.9 %, respectively. For MRI 1 and MRI 2 datasets, the average sensitivity and accuracy values are 99.1 %, 100 %, 75.6 % and 99.6 %, respectively. The average values for the Dice coefficient and Jaccard index for the CBCT 1 and CBCT 2 datasets are 95.88, 0.88, 0.6, and 0.51, respectively, while for MRI 1 and MRI 2 datasets, average values are 0.96, 0.93, 0.81 and 0.7, respectively, which confirms the high accuracy of the proposed method

Multiscale Musculoskeletal Modeling of the Lower Limb to Perform Personalized Simulations of Movement

Computational modeling has been used for many decades to inform design and decision-making in several fields of engineering, such as aerospace, automotive, petroleum, and others. However, it still struggles to have a similar impact in fields of medicine, such as orthopaedics. Three of the challenges that have limited the use of computational modeling in the clinical practice and in product development are model validation, personalization, and realism. Validation is a challenge because several internal parameters of the human body, such as muscle forces, are not safely measurable in vivo and, consequently, a thorough comparison between model outputs and experimental measurements is not always possible. Personalization is an additional issue because the inherent variability across a population needs to be accounted for in a model. Finally, the computational burden of simulations performed with a musculoskeletal model limits its level of realism. The purpose of the work presented in this dissertation is to investigate the applicability of state-of-the-art tools, and propose novel approaches to foster an evolution of computational modeling in orthopaedics. Specifically, (1) the reliability of the knee contact force predictions of a musculoskeletal model commonly used in the literature was analyzed using a global probabilistic analysis for three subjects with instrumented implants; (2) subject-specific and activity-specific moment arms of the muscles spanning the knee were estimated replacing the generic passive cadaveric motion implemented in the knee joint of a musculoskeletal model with in vivo kinematics measured from stereo-radiography images; (3) subject-specific joint mechanics for 6 total knee arthroplasty patients performing daily activities was estimated with a sequential multiscale modeling approach that combined joint loads estimated with a whole body musculoskeletal model, personalized joint geometries, and subject-specific fluoroscopy-measured kinematics; finally, (4) a closed-loop muscle control strategy was designed to track experimental joint kinematics and concurrently estimate muscle forces and knee mechanics with a finite element musculoskeletal model of the lower limb including a deformable representation of the joint. The utility of the modeling techniques proposed in this dissertation is presented within a clinical perspective in order to encourage the utilization of musculoskeletal modeling for clinical applications and product development

An Exploration into the Relationship between Knee Shape and Kinematics Before and After Total Knee Replacement

The knee joint is unique in its design and it is thought that its articular shape is the main driver of biomechanical behaviour. Although the shape of the bony knee is acknowledged to change with osteoarthritis, the specific relationship between shape changes and function is not well understood. Deep flexion, specifically kneeling, is an ideal testing environment for the tibiofemoral joint because it is both a difficult and a desirable activity for people with knee osteoarthritis. Total knee replacement (TKR) is a surgery which attempts to restore the articular shape in order to enhance function. However, the influence of implant design on kneeling kinematics is unclear. This thesis examines the role of knee shape on kneeling kinematics before and following total knee replacement. The four aims of this thesis were to: 1) describe and quantify the main modes of shape variation which distinguish end-stage OA from age- and sex-similar healthy knees; 2) determine whether bony shape can predict deep kneeling kinematics in people with and without OA; 3) examine the published literature to determine whether there are any differences in contact patterns as a function of TKR design; and 4) to prospectively compare the six-degree-of-freedom kneeling kinematics of posterior-stabilised fixed bearing, cruciate-retaining fixed bearing and cruciate retaining rotating platform designs. Statistical shape modelling identified differences between osteoarthritic and healthy bony knee shape. Specifically: large expansions around the femoral cartilage plate; expansion and depression at the medial tibial border; and an area of corresponding bony expansion on the posterior aspect of the medial femur and tibia. Statistical shape modelling and image registration derived six degree of freedom kinematics were used to test for associations between knee shape and kneeling kinematics. The kinematic variability was described using bivariate principle component analysis. While we found weak associations between knee shape and kinematics, BMI and group (OA vs Healthy) also predicted kneeling kinematics. This indicates that factors other than bony shape are important in predicting kneeling kinematics. The third study was a systematic review with meta-analyses using quality effects models which characterised the influence of TKR implant design on kneeling contact patterns. The review found posterior stabilised designs were different to cruciate retaining designs, but the heterogeneity was high limiting any firm conclusions. The final study was a prospective randomised clinical trial examining the influence of TKR design on kneeling kinematics. The study found that posterior-stabilised fixed-bearing and cruciate-retaining rotating-platform designs had higher maximal flexion compared to cruciate retaining-fixed bearing designs. Furthermore, posterior-stabilised fixed-bearing femoral components were more posterior and the cruciate-retaining rotating-platform was in more external femoral rotation throughout flexion. However, there was substantial between-patient variability. This research breaks new ground around which aspects of bony shape are altered in osteoarthritis and how these shapes, and prosthetic design, influence kneeling kinematics. Furthermore, the methodologies employed in this thesis provide new ways of describing the variability in complex shape and kinematics datasets, which may contribute to the identification of therapeutic efficacy. Knee shape is considered to be an important driver for normal movement. However, the results of this thesis indicate that there are potentially other factors, including soft-tissue properties and patient-specific movement strategies, which might influence the kinematics of deep kneeling. The message for surgeons and other clinicians is that bony shape and TKR design are not the primary drivers of functional performance and that kneeling should be on their radar as an activity to which their patients should aspire