An automated method to segment the femur for osteoarthritis research (original) (raw)
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IEEE transactions on bio-medical engineering, 2012
This paper presents a fully automated method for segmenting articular knee cartilage and bone from in vivo 3-D dual echo steady state images. The magnetic resonance imaging (MRI) datasets were obtained from the Osteoarthritis Initiative (OAI) pilot study and include longitudinal images from controls and subjects with knee osteoarthritis (OA) scanned twice at each visit (baseline, 24 month). Initially, human experts segmented six MRI series. Five of the six resultant sets served as reference atlases for a multiatlas segmentation algorithm. The methodology created precise knee segmentations that were used to extract articular cartilage volume, surface area, and thickness as well as subchondral bone plate curvature. Comparison to manual segmentation showed Dice similarity coefficient (DSC) of 0.88 and 0.84 for the femoral and tibial cartilage. In OA subjects, thickness measurements showed test-retest precision ranging from 0.014 mm (0.6%) at the femur to 0.038 mm (1.6%) at the femoral trochlea. In the same population, the curvature test-retest precision ranged from 0.0005 mm-1 (3.6%) at the femur to 0.0026 mm-1 (11.7%) at the medial tibia. Thickness longitudinal changes showed OA Pearson correlation coefficient of 0.94 for the femur. In conclusion, the fully automated segmentation methodology produces reproducible cartilage volume, thickness, and shape measurements valuable for the study of OA progression.
Osteoarthritis and Cartilage, 2014
Objective: To validate an automatic scheme for the segmentation and quantitative analysis of the medial meniscus (MM) and lateral meniscus (LM) in magnetic resonance (MR) images of the knee. Method: We analysed sagittal water-excited double-echo steady-state MR images of the knee from a subset of the Osteoarthritis Initiative (OAI) cohort. The MM and LM were automatically segmented in the MR images based on a deformable model approach. Quantitative parameters including volume, subluxation and tibial-coverage were automatically calculated for comparison (Wilcoxon tests) between knees with variable radiographic osteoarthritis (rOA), medial and lateral joint space narrowing (mJSN, lJSN) and pain. Automatic segmentations and estimated parameters were evaluated for accuracy using manual delineations of the menisci in 88 pathological knee MR examinations at baseline and 12 months time-points. Results: The median (95% confidence-interval (CI)) Dice similarity index (DSI) ð2*jAuto∩Manualj=ðjAutoj þ jManualjÞ*100Þ between manual and automated segmentations for the MM and LM volumes were 78.3% (75.0e78.7), 83.9% (82.1e83.9) at baseline and 75.3% (72.8e76.9), 83.0% (81.6e83.5) at 12 months. Pearson coefficients between automatic and manual segmentation parameters ranged from r ¼ 0.70 to r ¼ 0.92. MM in rOA/mJSN knees had significantly greater subluxation and smaller tibial-coverage than no-rOA/no-mJSN knees. LM in rOA knees had significantly greater volumes and tibial-coverage than no-rOA knees. Conclusion: Our automated method successfully segmented the menisci in normal and osteoarthritic knee MR images and detected meaningful morphological differences with respect to rOA and joint space narrowing (JSN). Our approach will facilitate analyses of the menisci in prospective MR cohorts such as the OAI for investigations into pathophysiological changes occurring in early osteoarthritis (OA) development.
Computers in biology and medicine, 2017
Knee osteoarthritis (OA) progression can be monitored by measuring changes in the subchondral bone structure such as area and shape from MR images as an imaging biomarker. However, measurements of these minute changes are highly dependent on the accurate segmentation of bone tissue from MR images and it is challenging task due to the complex tissue structure and inadequate image contrast/brightness. In this paper, a fully automated method for segmenting subchondral bone from knee MR images is proposed. Here, the contrast of knee MR images is enhanced using a gray-level S-curve transformation followed by automatic seed point detection using a three-dimensional multi-edge overlapping technique. Successively, bone regions are initially extracted using distance-regularized level-set evolution followed by identification and correction of leakages along the bone boundary regions using a boundary displacement technique. The performance of the developed technique is evaluated against ground...
Introduction: MRI bone surface area and femoral bone shape (B-score) measures have been employed as quantitative endpoints in DMOAD clinical trials. Computerized Tomography (CT) imaging is more commonly used for 3D visualization of bony anatomy due to its high bone-soft tissue contrast. We aimed to compare CT and MRI assessments of 3D imaging biomarkers. Methods: We used baseline and 24-month image data from the IMI-APPROACH 2-year prospective cohort study. Femur and tibia were automatically segmented using active appearance models to measure 3D bone shape, area and 3D joint space width (3DJSW). Linear regression was used to test for correlation between measures. Limits of agreement and bias were tested using Bland-Altman analysis. Results: CT-MR pairs of the same knee were available from 434 participants (78% female). B-scores from CT and MR were strongly correlated (R2 = 0.938) with minimal bias of 0.1. Area measures were also correlated but showed a consistent bias (MR smaller). ...
A morphological index for assessing hip osteoarthritis severity from radiographic images
A new method is proposed for assessing the severity of hip osteoarthritis (OA) based on radiographic hip joint space (HJS) morphology. 64 hips of patients with verified unilateral OA or bilateral OA were studied by digitizing the corresponding pelvic radiographs. Radiographic OA severity was assessed employing the Kellgren and Lawrence (KL) scale. Using custom-developed software, radiographs were enhanced, the margins of both HJSs were outlined, and 64 regions of interest (ROIs), corresponding to the delineated HJSs, were obtained. Employing custom-developed algorithms, an index (''joint space morphological index'' -JSMI) evaluating alterations in the shape and size of HJS was introduced, calculated and normalized with respect to each patient's individual anatomy. The JSMI values were used to introduce classification rules concerning the characterization of a hip in accordance with the KL scale. For each patient in the unilateral OA group, the OA severity was expressed as the percentage of the HJS area difference between the patient's osteoarthritic and contralateral normal hip. The per cent HJS area difference and the JSMI values were used in the design of a regression model for providing a quantitative estimation of OA severity. The per cent HJS area difference correlated highly with the pathological JSMI values (r520.83, p,0.001). The implementation of the JSMI-based classification rules resulted in high classification accuracies for characterizing hips as normal or osteoarthritic, 90.6% (95% exact confidence interval (CI): 80.7-96.5%), as well as for discriminating among OA severity categories, 91.7% (95% CI: 77.5-98.2%). Additionally, a simplified approach of JSMI calculation is suggested for daily clinical use. These JSMI values (JSMI simplified) were found not to differ significantly from (p.0.05), and to be strongly correlated with (r50.96, p,0.001), the corresponding ones obtained by the computerized approach. Additionally, the implementation of classification rules based on JSMI simplified resulted in classification accuracies identical to the corresponding ones obtained for the JSMI-based rules. The proposed method may be utilized for evaluating OA and monitoring OA progression.
Annals of the Rheumatic Diseases, 2020
ObjectivesOsteoarthritis (OA) structural status is imperfectly classified using radiographic assessment. Statistical shape modelling (SSM), a form of machine-learning, provides precise quantification of a characteristic 3D OA bone shape. We aimed to determine the benefits of this novel measure of OA status for assessing risks of clinically important outcomes.MethodsThe study used 4796 individuals from the Osteoarthritis Initiative cohort. SSM-derived femur bone shape (B-score) was measured from all 9433 baseline knee MRIs. We examined the relationship between B-score, radiographic Kellgren-Lawrence grade (KLG) and current and future pain and function as well as total knee replacement (TKR) up to 8 years.ResultsB-score repeatability supported 40 discrete grades. KLG and B-score were both associated with risk of current and future pain, functional limitation and TKR; logistic regression curves were similar. However, each KLG included a wide range of B-scores. For example, for KLG3, ri...
Proc. of SPIE Vol
Abnormal MR findings including cartilage defects, cartilage denuded areas, osteophytes, and bone marrow edema (BME) are used in staging and evaluating the degree of osteoarthritis (OA) in the knee. The locations of the abnormal findings have been correlated to the degree of pain and stiffness of the joint in the same location. The definition of the anatomic region in MR images is not always an objective task, due to the lack of clear anatomical features. This uncertainty causes variance in the location of the abnormality between readers and time points. Therefore, it is important to have a reproducible system to define the anatomic regions. This works present a computerized approach to define the different anatomic knee regions. The approach is based on an algorithm that uses unique features of the femur and its spatial relation in the extended knee. The femur features are found from three dimensional segmentation maps of the knee. From the segmentation maps, the algorithm automatically divides the femur cartilage into five anatomic regions: trochlea, medial weight bearing area, lateral weight bearing area, posterior medial femoral condyle, and posterior lateral femoral condyle. Furthermore, the algorithm automatically labels the medial and lateral tibia cartilage. The unsupervised definition of the knee regions allows a reproducible way to evaluate regional OA changes. This works will present the application of this automated algorithm for the regional analysis of the cartilage tissue.
Early detection of radiographic knee osteoarthritis using computer-aided analysis
Osteoarthritis and cartilage / OARS, Osteoarthritis Research Society, 2009
To determine whether computer-based analysis can detect features predictive of osteoarthritis (OA) development in radiographically normal knees. A systematic computer-aided image analysis method weighted neighbor distances using a compound hierarchy of algorithms representing morphology (WND-CHARM) was used to analyze pairs of weight-bearing knee X-rays. Initial X-rays were all scored as normal Kellgren-Lawrence (KL) grade 0, and on follow-up approximately 20 years later either developed OA (defined as KL grade=2) or remained normal. The computer-aided method predicted whether a knee would change from KL grade 0 to grade 3 with 72% accuracy (P<0.00001), and to grade 2 with 62% accuracy (P<0.01). Although a large part of the predictive signal comes from the image tiles that contained the joint, the region adjacent to the tibial spines provided the strongest predictive signal. Radiographic features detectable using a computer-aided image analysis method can predict the future de...