Inter-subject comparison of MRI knee cartilage thickness (original) (raw)
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Quantification and Visualization of MRI Cartilage of the Knee: A Simplified Approach
The estimation of articular cartilage of knee plays an important role in determining osteoarthritis (OA) level. The purpose of this study is to implement the segmentation, analysis and visualization techniques to characterize the knee cartilage in a simplified way. The segmentation technique used in this work is semi-automatic and based on Bezier splines and Canny edge detection. Cartilage edges are enhanced by using anisotropic diffusion to smooth the images. Shape-based interpolation is performed on the segmented cartilage in a simplified way for getting isotropic voxels. MRI registration is based on an approach which involves artificial matching of points at different slices. Analysis of the cartilage is then carried out by calculating its thickness and volume. Visualization of articular cartilage gives a supplementary tool to characterize it for quantification purpose.
Knee cartilage extraction and bone-cartilage interface analysis from 3D MRI data sets
Proc. SPIE Med. Imag. …, 2004
This works presents a robust methodology for the analysis of the knee joint cartilage and the knee bone-cartilage interface from fused MRI sets. The proposed approach starts by fusing a set of two 3D MR images the knee. Although the proposed method is not pulse sequence dependent, the first sequence should be programmed to achieve good contrast between bone and cartilage. The recommended second pulse sequence is one that maximizes the contrast between cartilage and surrounding soft tissues. Once both pulse sequences are fused, the proposed bone-cartilage analysis is done in four major steps. First, an unsupervised segmentation algorithm is used to extract the femur, the tibia, and the patella. Second, a knowledge based feature extraction algorithm is used to extract the femoral, tibia and patellar cartilages. Third, a trained user corrects cartilage missclassifications done by the automated extracted cartilage. Finally, the final segmentation is the revisited using an unsupervised MAP voxel relaxation algorithm. This final segmentation has the property that includes the extracted bone tissue as well as all the cartilage tissue. This is an improvement over previous approaches where only the cartilage was segmented. Furthermore, this approach yields very reproducible segmentation results in a set of scan-rescan experiments. When these segmentations were coupled with a partial volume compensated surface extraction algorithm the volume, area, thickness measurements shows precisions around 2.6%.
3rd IEEE International Symposium on Biomedical Imaging: Macro to Nano, 2006., 2006
Degeneration and loss of articular cartilage in Osteoarthritis (OA) is difficult to measure, because changes are small and localised. We present a method that uses statistical shape models of the knee bones to define an anatomically consistent frame of reference across a population, providing sensitive measures of cartilage morphology in anatomically equivalent regions of interest. Bone and cartilage were manually segmented from Magnetic Resonance Images (MRI) of volunteers' knees. Dense correspondences were defined across all subjects by constructing Minimum Description Length (MDL) statistical shape models of the bones. Regions of interest were manually delineated on the mean bone shapes provided by the models, and propagated to each individual in an anatomically consistent manner, using the model-based correspondences. We show that this approach results in precise measurements that can be used to detect small localised changes in cartilage thickness. Results are reported for an OA study, in which significant focal loss of cartilage was detected over 6 months in a cohort of just 31 patients.
Journal of Biomechanical Engineering, 2009
Cartilage morphology change is an important biomarker for the progression of osteoarthritis. The purpose of this study was to assess the accuracy of in vivo cartilage thickness measurements from MR image-based 3D cartilage models using a laser scanning method and to test if the accuracy changes with cartilage thickness. Three-dimensional tibial cartilage models were created from MR images (in-plane resolution of 0.55 mm and thickness of 1.5 mm) of osteoarthritic knees of ten patients prior to total knee replacement surgery using a semi-automated B-spline segmentation algorithm. Following surgery, the resected tibial plateaus were laser scanned and made into 3D models. The MR image and laser-scan based models were registered to each other using a shape matching technique. The thicknesses were compared point wise for the overall surface. The linear mixed-effects model was used for statistical test. On average, taking account of individual variations, the thickness measurements in MRI ...
Osteoarthritis and cartilage / OARS, Osteoarthritis Research Society, 2006
To aid in detection of osteoarthritis (OA) progression in serial magnetic resonance (MR) scans, we assessed feasibility and accuracy of rapid 3D image registration of the tibial plateau in normal and arthritic subjects, and inter-scan reliability of semi-automated cartilage volume measurement from these images. Two T1 fat-suppressed knee MR scans were obtained 2 weeks apart in healthy adults (n = 9, age 23-48 years). Four scans of each of three patients with established OA were obtained over 2 years. At baseline, the tibial surface was digitized by semi-automated edge detection and medial tibial plateau cartilage volume was calculated from high-intensity voxels within a manually drawn region of interest (ROI). In subsequent scans, the digitized tibial surface was registered to the baseline location by photogrammetric 3D coordinate transformation, and cartilage volume was automatically recalculated by reuse of the ROI. We measured registration accuracy by root mean square (RMS) dista...
Magnetic Resonance Imaging-Based Estimation of Knee Cartilage Thickness with MATLAB
Jurnal Kejuruteraan, 2021
Detection of early knee osteoarthritis remains a driving force in the search for more promising quantitative assessment approaches. Apart from other conventional methods such as radiography, computed tomography, and sonography, magnetic resonance imaging has become more widely available and has made it essential to visualize the knee's entire anatomy. Biomarkers such as joint space narrowing, articular cartilage thickness, cartilage volume, cartilage surface curvature, lesion depth, and others are used to determine disease progression in non-invasive manner. In this research, a regional cartilage normal thickness approximation (RCN-ta) model was developed with MATLAB to enable rapid cartilage thickness assessment with a simple click. The model formulated was compared to the FDA-cleared software measurements. A reasonable range of 0.135-0.214 mm of root-mean-square error may be predicted from the model. With a high ICC > 0.975, the model was highly accurate and reproducible. A good agreement between the proposed model and the medically used software can be found with a high Pearson correlation of r > 0.90.
IEEE Transactions on Medical Imaging, 2000
In this paper, we present a segmentation scheme that automatically and accurately segments all the cartilages from magnetic resonance (MR) images of nonpathological knees. Our scheme involves the automatic segmentation of the bones using a three-dimensional active shape model, the extraction of the expected bone-cartilage interface (BCI), and cartilage segmentation from the BCI using a deformable model that utilizes localization, patient specific tissue estimation and a model of the thickness variation. The accuracy of this scheme was experimentally validated using leave one out experiments on a database of fat suppressed spoiled gradient recall MR images. The scheme was compared to three state of the art approaches, tissue classification, a modified semi-automatic watershed algorithm and nonrigid registration (B-spline based free form deformation). Our scheme obtained an average Dice similarity coefficient (DSC) of (0.83, 0.83, 0.85) for the (patellar, tibial, femoral) cartilages, while (0.82, 0.81, 0.86) was obtained with a tissue classifier and (0.73, 0.79, 0.76) was obtained with nonrigid registration. The average DSC obtained for all the cartilages using a semi-automatic watershed algorithm (0.90) was slightly higher than our approach (0.89), however unlike this approach we segment each cartilage as a separate object. The effectiveness of our approach for quantitative analysis was evaluated using volume and thickness measures with a median volume difference error of (5.92, 4.65, 5.69) and absolute Laplacian thickness difference of (0.13, 0.24, 0.12) mm.
Magnetic Resonance in Medicine, 1997
Previously reported studies to quantify articular cartilage have used labour-intensive manual or semi-automatic data-driven techniques, demonstrating high accuracy and precision. However, none has been able to automate the segmentation process. This paper describes a fast, automatic, modelbased approach to segmentation and thickness measurement of the femoral cartilage in 3D T1-weighted images using Active Shape Models (ASMs). Systematic experiments were performed to assess the accuracy and precision of the technique with in vivo images of both normal and abnormal knees.