The use of active shape models for making thickness measurements of articular cartilage from MR images

30 Years of MRI-based cartilage & bone morphometry in knee osteoarthritis: From correlation to clinical trials

OBJECTIVE

The first publication on morphometric analysis of articular cartilage using magnetic resonance imaging (MRI) in 1994 set the scene for a game change in osteoarthritis (OA) research. The current review highlights milestones in cartilage and bone morphometry, summarizing the rapid progress made in imaging, its application to understanding joint (patho-)physiology, and its use in interventional clinical trials.

METHODS

Based on a Pubmed search of articles from 1994 to 2023, the authors subjectively selected representative work illustrating important steps in the development or application of magnetic resonance-based cartilage and bone morphometry, with a focus on studies in humans, and on the knee. Research on OA-pathophysiology is addressed only briefly, given length constraints. Compositional and semi-quantitative assessment are not covered here.

RESULTS

The selected articles are presented in historical order as well as by content. We review progress in the technical aspects of image acquisition, segmentation and analysis, advances in understanding tissue growth, physiology, function, and adaptation, and a selection of clinical trials examining the efficacy of interventions on knee cartilage and bone. A perspective is provided of how lessons learned may be applied to future research and clinical management.

CONCLUSIONS

Over the past 30 years, MRI-based morphometry of cartilage and bone has contributed to a paradigm shift in understanding articular tissue physiology and OA pathophysiology, and to the development of new treatment strategies. It is likely that these technologies will continue to play a key role in the development and (accelerated) approval of therapy, potentially targeted to different OA phenotypes.

A Comprehensive Review on MRI-based Knee Joint Segmentation and Analysis Techniques

Using magnetic resonance imaging (MRI) in osteoarthritis pathogenesis research has proven extremely beneficial. However, it is always challenging for both clinicians and researchers to detect morphological changes in knee joints from magnetic resonance (MR) imaging since the surrounding tissues produce identical signals in MR studies, making it difficult to distinguish between them. Segmenting the knee bone, articular cartilage and menisci from the MR images allows one to examine the complete volume of the bone, articular cartilage, and menisci. It can also be used to assess certain characteristics quantitatively. However, segmentation is a laborious and time-consuming operation that requires sufficient training to complete correctly. With the advancement of MRI technology and computational methods, researchers have developed several algorithms to automate the task of individual knee bone, articular cartilage and meniscus segmentation during the last two decades. This systematic review aims to present available fully and semi-automatic segmentation methods for knee bone, cartilage, and meniscus published in different scientific articles. This review provides a vivid description of the scientific advancements to clinicians and researchers in this field of image analysis and segmentation, which helps the development of novel automated methods for clinical applications. The review also contains the recently developed fully automated deep learning-based methods for segmentation, which not only provides better results compared to the conventional techniques but also open a new field of research in Medical Imaging.

Fully Automatic Knee Bone Detection and Segmentation on Three-Dimensional MRI

In the medical sector, three-dimensional (3D) images are commonly used like computed tomography (CT) and magnetic resonance imaging (MRI). The 3D MRI is a non-invasive method of studying the soft-tissue structures in a knee joint for osteoarthritis studies. It can greatly improve the accuracy of segmenting structures such as cartilage, bone marrow lesion, and meniscus by identifying the bone structure first. U-net is a convolutional neural network that was originally designed to segment the biological images with limited training data. The input of the original U-net is a single 2D image and the output is a binary 2D image. In this study, we modified the U-net model to identify the knee bone structures using 3D MRI, which is a sequence of 2D slices. A fully automatic model has been proposed to detect and segment knee bones. The proposed model was trained, tested, and validated using 99 knee MRI cases where each case consists of 160 2D slices for a single knee scan. To evaluate the model's performance, the similarity, dice coefficient (DICE), and area error metrics were calculated. Separate models were trained using different knee bone components including tibia, femur, patella, as well as a combined model for segmenting all the knee bones. Using the whole MRI sequence (160 slices), the method was able to detect the beginning and ending bone slices first, and then segment the bone structures for all the slices in between. On the testing set, the detection model accomplished 98.79% accuracy and the segmentation model achieved DICE 96.94% and similarity 93.98%. The proposed method outperforms several state-of-the-art methods, i.e., it outperforms U-net by 3.68%, SegNet by 14.45%, and FCN-8 by 2.34%, in terms of DICE score using the same dataset.

Quantification in Musculoskeletal Imaging Using Computational Analysis and Machine Learning: Segmentation and Radiomics

Although still limited in clinical practice, quantitative analysis is expected to increase the value of musculoskeletal (MSK) imaging. Segmentation aims at isolating the tissues and/or regions of interest in the image and is crucial to the extraction of quantitative features such as size, signal intensity, or image texture. These features may serve to support the diagnosis and monitoring of disease. Radiomics refers to the process of extracting large amounts of features from radiologic images and combining them with clinical, biological, genetic, or any other type of complementary data to build diagnostic, prognostic, or predictive models. The advent of machine learning offers promising prospects for automatic segmentation and integration of large amounts of data. We present commonly used segmentation methods and describe the radiomics pipeline, highlighting the challenges to overcome for adoption in clinical practice. We provide some examples of applications from the MSK literature.

pyKNEEr: An image analysis workflow for open and reproducible research on femoral knee cartilage

Transparent research in musculoskeletal imaging is fundamental to reliably investigate diseases such as knee osteoarthritis (OA), a chronic disease impairing femoral knee cartilage. To study cartilage degeneration, researchers have developed algorithms to segment femoral knee cartilage from magnetic resonance (MR) images and to measure cartilage morphology and relaxometry. The majority of these algorithms are not publicly available or require advanced programming skills to be compiled and run. However, to accelerate discoveries and findings, it is crucial to have open and reproducible workflows. We present pyKNEEr, a framework for open and reproducible research on femoral knee cartilage from MR images. pyKNEEr is written in python, uses Jupyter notebook as a user interface, and is available on GitHub with a GNU GPLv3 license. It is composed of three modules: 1) image preprocessing to standardize spatial and intensity characteristics; 2) femoral knee cartilage segmentation for intersubject, multimodal, and longitudinal acquisitions; and 3) analysis of cartilage morphology and relaxometry. Each module contains one or more Jupyter notebooks with narrative, code, visualizations, and dependencies to reproduce computational environments. pyKNEEr facilitates transparent image-based research of femoral knee cartilage because of its ease of installation and use, and its versatility for publication and sharing among researchers. Finally, due to its modular structure, pyKNEEr favors code extension and algorithm comparison. We tested our reproducible workflows with experiments that also constitute an example of transparent research with pyKNEEr, and we compared pyKNEEr performances to existing algorithms in literature review visualizations. We provide links to executed notebooks and executable environments for immediate reproducibility of our findings.

Fully automated patellofemoral MRI segmentation using holistically nested networks: Implications for evaluating patellofemoral osteoarthritis, pain, injury, pathology, and adolescent development

PURPOSE

Our clinical understanding of the relationship between 3D bone morphology and knee osteoarthritis, as well as our ability to investigate potential causative factors of osteoarthritis, has been hampered by the time-intensive nature of manually segmenting bone from MR images. Thus, we aim to develop and validate a fully automated deep learning framework for segmenting the patella and distal femur cortex, in both adults and actively growing adolescents.

METHODS

Data from 93 subjects, obtained from on institutional review board-approved protocol, formed the study database. 3D sagittal gradient recalled echo and gradient recalled echo with fat saturation images and manual models of the outer cortex were available for 86 femurs and 90 patellae. A deep-learning-based 2D holistically nested network (HNN) architecture was developed to automatically segment the patella and distal femur using both single (sagittal, uniplanar) and 3 cardinal plane (triplanar) methodologies. Errors in the surface-to-surface distances and the Dice coefficient were the primary measures used to quantitatively evaluate segmentation accuracy using a 9-fold cross-validation.

RESULTS

Average absolute errors for segmenting both the patella and femur were 0.33 mm. The Dice coefficients were 97% and 94% for the femur and patella. The uniplanar, relative to the triplanar, methodology produced slightly superior segmentation. Neither the presence of active growth plates nor pathology influenced segmentation accuracy.

CONCLUSION

The proposed HNN with multi-feature architecture provides a fully automatic technique capable of delineating the often indistinct interfaces between the bone and other joint structures with an accuracy better than nearly all other techniques presented previously, even when active growth plates are present.

Knee-Cartilage Segmentation and Thickness Measurement from 2D Ultrasound

Ultrasound (US) could become a standard of care imaging modality for the quantitative assessment of femoral cartilage thickness for the early diagnosis of knee osteoarthritis. However, low contrast, high levels of speckle noise, and various imaging artefacts hinder the analysis of collected data. Accurate, robust, and fully automatic US image-enhancement and cartilage-segmentation methods are needed in order to improve the widespread deployment of this imaging modality for knee-osteoarthritis diagnosis and monitoring. In this work, we propose a method based on local-phase-based image processing for automatic knee-cartilage image enhancement, segmentation, and thickness measurement. A local-phase feature-guided dynamic-programming approach is used for the fully automatic localization of knee-bone surfaces. The localized bone surfaces are used as seed points for automating the seed-guided segmentation of the cartilage. We evaluated the Random Walker (RW), watershed, and graph-cut-based segmentation methods from 200 scans obtained from ten healthy volunteers. Validation against manual expert segmentation achieved a mean dice similarity coefficient of 0.90, 0.86, and 0.84 for the RW, watershed, and graph-cut segmentation methods, respectively. Automatically segmented cartilage regions achieved 0.18 mm localization accuracy compared to manual expert thickness measurement.

Quantitative susceptibility mapping of articular cartilage in patients with osteoarthritis at 3T

BACKGROUND

Quantitative susceptibility mapping (QSM) has recently been applied in humans to quantify the magnetic susceptibility of collagen fibrils in the articular cartilage.

PURPOSE

To determine the ability of QSM to detect cartilage matrix degeneration between normal and early knee osteoarthritis (OA) patients.

STUDY TYPE

Prospective.

POPULATION

Twenty-four patients with knee OA and 24 age- and sex-matched healthy controls.

FIELD STRENGTH/SEQUENCE

3D gradient echo, T₁ turbo spin echo, and proton density-weighted (PDw) spectral attenuated inversion recovery (SPAIR) sequence at 3.0T.

ASSESSMENT

Scan-rescan reproducibility of the susceptibility values in the cartilage was assessed in control subjects. Cartilage thickness, volume, mean, and standard deviation (SD) of susceptibility values of the cartilage compartments were compared between normal and OA patients. The relationship between magnetic susceptibility values and cartilage lesion grading based on MR images was studied.

STATISTICAL TESTS

The Wilcoxon Rank-Sum test was used to compare cartilage thickness, volume, mean, and SD of susceptibility values between control subjects and OA patients. A Spearman rank correlation was performed to study the relationship between the mean and SD of susceptibility values and the cartilage thinning grades.

RESULTS

The SD of magnetic susceptibility values in the knee cartilage was significantly lower in OA patients compared with healthy controls, and it decreased with more severe MR grades of cartilage thinning degeneration. Significant correlations between the SD of susceptibility values and cartilage thinning grades were observed with R²  = 0.64 and P = 0.000, R²  = 0.47 and P = 0.002, R²  = 0.52 and P = 0.001, R²  = 0.42 and P = 0.0006, and R²  = 0.67 and P = 0.000 for medial femoral condyle (MFC), lateral femoral condyle (LFC), medial tibia (MT), lateral tibia (LT), and patella, respectively. No significant difference was found in cartilage volume (P = 0.17, P = 0.13, P = 0.20, P = 0.25, and P = 0.18 for MFC, LFC, MT, LT, and patella, respectively) and thickness (P = 0.31, P = 0.19, P = 0.16, P = 0.09, and P = 0.22 for MFC, LFC, MT, LT, and patella, respectively) between OA patients and healthy controls.

DATA CONCLUSION

The variations of susceptibility values in the knee cartilage decrease with the degree of cartilage degeneration. QSM may be a sensitive indicator for alteration of the collagen network and shows potential to detect cartilage degeneration at early stage.

LEVEL OF EVIDENCE

2 Technical Efficacy: Stage 3 J. Magn. Reson. Imaging 2018.

Vector Field Convolution-Based B-Spline Deformation Model for 3D Segmentation of Cartilage in MRI

In this paper, a novel 3D vector field convolution (VFC)-based B-spline deformation model is proposed for accurate and robust cartilage segmentation. Firstly, the anisotropic diffusion method is utilized for noise reduction, and the Sinc interpolation method is employed for resampling. Then, to extract the rough cartilage, features derived from

Knee cartilage segmentation and thickness computation from ultrasound images

Quantitative thickness computation of knee cartilage in ultrasound images requires segmentation of a monotonous hypoechoic band between the soft tissue-cartilage interface and the cartilage-bone interface. Speckle noise and intensity bias captured in the ultrasound images often complicates the segmentation task. This paper presents knee cartilage segmentation using locally statistical level set method (LSLSM) and thickness computation using normal distance. Comparison on several level set methods in the attempt of segmenting the knee cartilage shows that LSLSM yields a more satisfactory result. When LSLSM was applied to 80 datasets, the qualitative segmentation assessment indicates a substantial agreement with Cohen's κ coefficient of 0.73. The quantitative validation metrics of Dice similarity coefficient and Hausdorff distance have average values of 0.91 ± 0.01 and 6.21 ± 0.59 pixels, respectively. These satisfactory segmentation results are making the true thickness between two interfaces of the cartilage possible to be computed based on the segmented images. The measured cartilage thickness ranged from 1.35 to 2.42 mm with an average value of 1.97 ± 0.11 mm, reflecting the robustness of the segmentation algorithm to various cartilage thickness. These results indicate a potential application of the methods described for assessment of cartilage degeneration where changes in the cartilage thickness can be quantified over time by comparing the true thickness at a certain time interval.

Comparison of Different Approaches for Measuring Tibial Cartilage Thickness

Osteoarthritis is a degenerative disease affecting bones and cartilage especially in the human knee. In this context, cartilage thickness is an indicator for knee cartilage health. Thickness measurements are performed on medical images acquired in-vivo. Currently, there is no standard method agreed upon that defines a distance measure in articular cartilage. In this work, we present a comparison of different methods commonly used in literature. These methods are based on nearest neighbors, surface normal vectors, local thickness and potential field lines. All approaches were applied to manual segmentations of tibia and lateral and medial tibial cartilage performed by experienced raters. The underlying data were contrast agent-enhanced cone-beam C-arm CT reconstructions of one healthy subject's knee. The subject was scanned three times, once in supine position and two times in a standing weight-bearing position. A comparison of the resulting thickness maps shows similar distributions and high correlation coefficients between the approaches above 0.90. The nearest neighbor method results on average in the lowest cartilage thickness values, while the local thickness approach assigns the highest values. We showed that the different methods agree in their thickness distribution. The results will be used for a future evaluation of cartilage change under weight-bearing conditions.

Modeling the shape and composition of the human body using dual energy X-ray absorptiometry images

There is growing evidence that body shape and regional body composition are strong indicators of metabolic health. The purpose of this study was to develop statistical models that accurately describe holistic body shape, thickness, and leanness. We hypothesized that there are unique body shape features that are predictive of mortality beyond standard clinical measures. We developed algorithms to process whole-body dual-energy X-ray absorptiometry (DXA) scans into body thickness and leanness images. We performed statistical appearance modeling (SAM) and principal component analysis (PCA) to efficiently encode the variance of body shape, leanness, and thickness across sample of 400 older Americans from the Health ABC study. The sample included 200 cases and 200 controls based on 6-year mortality status, matched on sex, race and BMI. The final model contained 52 points outlining the torso, upper arms, thighs, and bony landmarks. Correlation analyses were performed on the PCA parameters to identify body shape features that vary across groups and with metabolic risk. Stepwise logistic regression was performed to identify sex and race, and predict mortality risk as a function of body shape parameters. These parameters are novel body composition features that uniquely identify body phenotypes of different groups and predict mortality risk. Three parameters from a SAM of body leanness and thickness accurately identified sex (training AUC = 0.99) and six accurately identified race (training AUC = 0.91) in the sample dataset. Three parameters from a SAM of only body thickness predicted mortality (training AUC = 0.66, validation AUC = 0.62). Further study is warranted to identify specific shape/composition features that predict other health outcomes.

The relationship between three-dimensional knee MRI bone shape and total knee replacement-a case control study: data from the Osteoarthritis Initiative

Objective. There is growing understanding of the importance of bone in OA. Our aim was to determine the relationship between 3D MRI bone shape and total knee replacement (TKR).

Methods. A nested case-control study within the Osteoarthritis Initiative cohort identified case knees with confirmed TKR for OA and controls that were matched using propensity scores. Active appearance modelling quantification of the bone shape of all knee bones identified vectors between knees having or not having OA. Vectors were scaled such that −1 and +1 represented the mean non-OA and mean OA shapes.

Results. Compared to controls (n = 310), TKR cases (n = 310) had a more positive mean baseline 3D bone shape vector, indicating more advanced structural OA, for the femur [mean 0.98 vs −0.11; difference (95% CI) 1.10 (0.88, 1.31)], tibia [mean 0.86 vs −0.07; difference (95% CI) 0.94 (0.72, 1.16)] and patella [mean 0.95 vs 0.03; difference (95% CI) 0.92 (0.65, 1.20)]. Odds ratios (95% CI) for TKR per normalized unit of 3D bone shape vector for the femur, tibia and patella were: 1.85 (1.59, 2.16), 1.64 (1.42, 1.89) and 1.36 (1.22, 1.50), respectively, all P < 0.001. After including Kellgren–Lawrence grade in a multivariable analysis, only the femur 3D shape vector remained significantly associated with TKR [odds ratio 1.24 (1.02, 1.51)].

Conclusion. 3D bone shape was associated with the endpoint of this study, TKR, with femoral shape being most associated. This study contributes to the validation of quantitative MRI bone biomarkers for OA structure-modification trials.

Segmentation of joint and musculoskeletal tissue in the study of arthritis

As the most frequent cause of physical disability, musculoskeletal diseases such as arthritis and osteoporosis have a great social and economical impact. Quantitative magnetic resonance imaging (MRI) biomarkers are important tools that allow clinicians to better characterize, monitor, and even predict musculoskeletal disease progression. Post-processing pipelines often include image segmentation. Manually identifying the border of the region of interest (ROI) is a difficult and time-consuming task. Manual segmentation is also affected by inter- and intrauser variability, thus limiting standardization. Fully automatic or semi-automatic methods that minimize the user interaction are highly desirable. Unfortunately, an ultimate, highly reliable and extensively evaluated solution for joint and musculoskeletal tissue segmentation has not yet been proposed, and many clinical studies still adopt fully manual procedures. Moreover, the clinical translation of several promising quantitative MRI techniques is highly affected by the lack of an established, fast, and accurate segmentation method. The goal of this review is to present some of the techniques proposed in recent literature that have been adopted in clinical studies for joint and musculoskeletal tissue analyses in arthritis patients. The most widely used MRI sequences and image processing algorithms employed to accomplish segmentation challenges will be discussed in this paper.

Femoral cartilage segmentation in knee MRI scans using two stage voxel classification

Using more than one classification stage and exploiting class population imbalance allows for incorporating powerful classifiers in tasks requiring large scale training data, even if these classifiers scale badly with the number of training samples. This led us to propose a two-stage classifier for segmenting tibial cartilage in knee MRI scans combining nearest neighbor classification and support vector machines (SVMs). Here we apply it to femoral cartilage segmentation. We describe the similarities and differences between segmenting these two knee cartilages. For further speeding up batch SVM training, we propose loosening the stopping condition in the quadratic program solver before considering moving on to other approximation techniques such as online SVMs. The two-stage approach reached a higher accuracy in comparison to the one-stage state-of-the-art method. It also achieved better inter-scan segmentation reproducibility when compared to a radiologist as well as the current state-of-the-art method.

Automatic segmentation of high- and low-field knee MRIs using knee image quantification with data from the osteoarthritis initiative

Clinical studies including thousands of magnetic resonance imaging (MRI) scans offer potential for pathogenesis research in osteoarthritis. However, comprehensive quantification of all bone, cartilage, and meniscus compartments is challenging. We propose a segmentation framework for fully automatic segmentation of knee MRI. The framework combines multiatlas rigid registration with voxel classification and was trained on manual segmentations with varying configurations of bones, cartilages, and menisci. The validation included high- and low-field knee MRI cohorts from the Center for Clinical and Basic Research, the osteoarthritis initiative (QAI), and the segmentation of knee images10 (SKI10) challenge. In total, 1907 knee MRIs were segmented during the evaluation. No segmentations were excluded. Our resulting OAI cartilage volume scores are available upon request. The precision and accuracy performances matched manual reader re-segmentation well. The cartilage volume scan-rescan precision was 4.9% (RMS CV). The Dice volume overlaps in the medial/lateral tibial/femoral cartilage compartments were 0.80 to 0.87. The correlations with volumes from independent methods were between 0.90 and 0.96 on the OAI scans. Thus, the framework demonstrated precision and accuracy comparable to manual segmentations. Finally, our method placed second for cartilage segmentation in the SKI10 challenge. The comprehensive validation suggested that automatic segmentation is appropriate for cohorts with thousands of scans.

Incorporating population-level variability in orthopedic biomechanical analysis: a review

Effectively addressing population-level variability within orthopedic analyses requires robust data sets that span the target population and can be greatly facilitated by statistical methods for incorporating such data into functional biomechanical models. Data sets continue to be disseminated that include not just anatomical information but also key mechanical data including tissue or joint stiffness, gait patterns, and other inputs relevant to analysis of joint function across a range of anatomies and physiologies. Statistical modeling can be used to establish correlations between a variety of structural and functional biometrics rooted in these data and to quantify how these correlations change from health to disease and, finally, to joint reconstruction or other clinical intervention. Principal component analysis provides a basis for effectively and efficiently integrating variability in anatomy, tissue properties, joint kinetics, and kinematics into mechanistic models of joint function. With such models, bioengineers are able to study the effects of variability on biomechanical performance, not just on a patient-specific basis but in a way that may be predictive of a larger patient population. The goal of this paper is to demonstrate the broad use of statistical modeling within orthopedics and to discuss ways to continue to leverage these techniques to improve biomechanical understanding of orthopedic systems across populations.

Acetabular cartilage segmentation in CT arthrography based on a bone-normalized probabilistic atlas

PURPOSE

Determination of acetabular cartilage loss in the hip joint is a clinically significant metric that requires image segmentation. A new semiautomatic method to segment acetabular cartilage in computed tomography (CT) arthrography scans was developed and tested.

METHODS

A semiautomatic segmentation method was developed based on the combination of anatomical and statistical information. Anatomical information is identified using the pelvic bone position and the contact area between cartilage and bone. Statistical information is acquired from CT intensity modeling of acetabular cartilage and adjacent tissue structures. This method was applied to the identification of acetabular cartilages in 37 intra-articular CT arthrography scans.

RESULTS

The semiautomatic anatomical-statistical method performed better than other segmentation methods. The semiautomatic method was effective in noisy scans and was able to detect damaged cartilage.

CONCLUSIONS

The new semiautomatic method segments acetabular cartilage by fully utilizing the statistical and anatomical information in CT arthrography datasets. This method for hip joint cartilage segmentation has potential for use in many clinical applications.

Deep feature learning for knee cartilage segmentation using a triplanar convolutional neural network

Segmentation of anatomical structures in medical images is often based on a voxel/pixel classification approach. Deep learning systems, such as convolutional neural networks (CNNs), can infer a hierarchical representation of images that fosters categorization. We propose a novel system for voxel classification integrating three 2D CNNs, which have a one-to-one association with the xy, yz and zx planes of 3D image, respectively. We applied our method to the segmentation of tibial cartilage in low field knee MRI scans and tested it on 114 unseen scans. Although our method uses only 2D features at a single scale, it performs better than a state-of-the-art method using 3D multi-scale features. In the latter approach, the features and the classifier have been carefully adapted to the problem at hand. That we were able to get better results by a deep learning architecture that autonomously learns the features from the images is the main insight of this study.

Magnetic resonance imaging-based three-dimensional bone shape of the knee predicts onset of knee osteoarthritis: data from the osteoarthritis initiative

OBJECTIVE

To examine whether magnetic resonance imaging (MRI)-based 3-dimensional (3-D) bone shape predicts the onset of radiographic knee osteoarthritis (OA).

METHODS

We conducted a case-control study using data from the Osteoarthritis Initiative by identifying knees that developed incident tibiofemoral radiographic knee OA (case knees) during followup, and matching them each to 2 random control knees. Using knee MRIs, we performed active appearance modeling of the femur, tibia, and patella and linear discriminant analysis to identify vectors that best classified knees with OA versus those without OA. Vectors were scaled such that -1 and +1 represented the mean non-OA and mean OA shapes, respectively. We examined the relation of 3-D bone shape to incident OA (new-onset Kellgren and Lawrence [K/L] grade ≥2) occurring 12 months later using conditional logistic regression.

RESULTS

A total of 178 case knees (incident OA) were matched to 353 control knees. The whole joint (i.e., tibia, femur, and patella) 3-D bone shape vector had the strongest magnitude of effect, with knees in the highest tertile having a 3.0 times higher likelihood of developing incident radiographic knee OA 12 months later compared with those in the lowest tertile (95% confidence interval [95% CI] 1.8-5.0, P < 0.0001). The associations were even stronger among knees that had completely normal radiographs before incidence (K/L grade of 0) (odds ratio 12.5 [95% CI 4.0-39.3]). Bone shape at baseline, often several years before incidence, predicted later OA.

CONCLUSION

MRI-based 3-D bone shape predicted the later onset of radiographic OA. Further study is warranted to determine whether such methods can detect treatment effects in trials and provide insight into the pathophysiology of OA development.

Advances in imaging of osteoarthritis and cartilage

Osteoarthritis (OA) is the most frequent form of arthritis, with major implications for individual and public health care without effective treatment available. The field of joint imaging, and particularly magnetic resonance (MR) imaging, has evolved rapidly owing to technical advances and the application of these to the field of clinical research. Cartilage imaging certainly is at the forefront of these developments. In this review, the different aspects of OA imaging and cartilage assessment, with an emphasis on recent advances, will be presented. The current role of radiography, including advances in the technology for joint space width assessment, will be discussed. The development of various MR imaging techniques capable of facilitating assessment of cartilage morphology and the methods for evaluating the biochemical composition of cartilage will be presented. Advances in quantitative morphologic cartilage assessment and semiquantitative whole-organ assessment will be reviewed. Although MR imaging is the most important modality in imaging of OA and cartilage, others such as ultrasonography play a complementary role that will be discussed briefly.

The evolution of articular cartilage imaging and its impact on clinical practice

Over the past four decades, articular cartilage imaging has developed rapidly. Imaging now plays a critical role not only in clinical practice and therapeutic decisions but also in the basic research probing our understanding of cartilage physiology and biomechanics.

Quantification of cartilage loss in local regions of knee joints using semi-automated segmentation software: analysis of longitudinal data from the Osteoarthritis Initiative (OAI)

INTRODUCTION

Quantitative cartilage morphometry is a valuable tool to assess osteoarthritis (OA) progression. Current methodologies generally evaluate cartilage morphometry in a full or partial sub-region of the cartilage plates. This report describes the evaluation of a semi-automated cartilage segmentation software tool capable of quantifying cartilage loss in a local indexed region.

METHODS

We examined the baseline and 24-month follow-up MRI image sets of twenty-four subjects from the progression cohort of Osteoarthritis Initiative (OAI), using the Kellgren-Lawrence (KL) score of 3 at baseline as the inclusion criteria. A radiologist independently marked a single region of local thinning for each subject, and three additional readers, blinded to time point, segmented the cartilage using a semi-automated software method. Each baseline-24-month segmentation pair was then registered in 3D and the change in cartilage volume was measured.

RESULTS

After 3D registration, the change in cartilage volume was calculated in specified regions centered at the marked point, and for the entire medial compartment of femur. The responsiveness was quantified using the standardized response mean (SRM) values and the percentage of subjects that showed a loss in cartilage volume. The most responsive measure of change was SRM=-1.21, and was found for a region of 10mm from the indexed point.

DISCUSSION

The results suggest that measurement of cartilage loss in a local region is superior to larger areas and to the total plate. There also may be an optimal region size (10mm from an indexed point) in which to measure change. In principle, the method is substantially faster than segmenting entire plates or sub-regions.

Quantitative cartilage imaging in knee osteoarthritis

Quantitative measures of cartilage morphology (i.e., thickness) represent potentially powerful surrogate endpoints in osteoarthritis (OA). These can be used to identify risk factors of structural disease progression and can facilitate the clinical efficacy testing of structure modifying drugs in OA. This paper focuses on quantitative imaging of articular cartilage morphology in the knee, and will specifically deal with different cartilage morphology outcome variables and regions of interest, the relative performance and relationship between cartilage morphology measures, reference values for MRI-based knee cartilage morphometry, imaging protocols for measurement of cartilage morphology (including those used in the Osteoarthritis Initiative), sensitivity to change observed in knee OA, spatial patterns of cartilage loss as derived by subregional analysis, comparison of MRI changes with radiographic changes, risk factors of MRI-based cartilage loss in knee OA, the correlation of MRI-based cartilage loss with clinical outcomes, treatment response in knee OA, and future directions of the field.

LOGISMOS--layered optimal graph image segmentation of multiple objects and surfaces: cartilage segmentation in the knee joint

A novel method for simultaneous segmentation of multiple interacting surfaces belonging to multiple interacting objects, called LOGISMOS (layered optimal graph image segmentation of multiple objects and surfaces), is reported. The approach is based on the algorithmic incorporation of multiple spatial inter-relationships in a single n-dimensional graph, followed by graph optimization that yields a globally optimal solution. The LOGISMOS method's utility and performance are demonstrated on a bone and cartilage segmentation task in the human knee joint. Although trained on only a relatively small number of nine example images, this system achieved good performance. Judged by dice similarity coefficients (DSC) using a leave-one-out test, DSC values of 0.84 ± 0.04, 0.80 ± 0.04 and 0.80 ± 0.04 were obtained for the femoral, tibial, and patellar cartilage regions, respectively. These are excellent DSC values, considering the narrow-sheet character of the cartilage regions. Similarly, low signed mean cartilage thickness errors were obtained when compared to a manually-traced independent standard in 60 randomly selected 3-D MR image datasets from the Osteoarthritis Initiative database-0.11 ± 0.24, 0.05 ± 0.23, and 0.03 ± 0.17 mm for the femoral, tibial, and patellar cartilage thickness, respectively. The average signed surface positioning errors for the six detected surfaces ranged from 0.04 ± 0.12 mm to 0.16 ± 0.22 mm. The reported LOGISMOS framework provides robust and accurate segmentation of the knee joint bone and cartilage surfaces of the femur, tibia, and patella. As a general segmentation tool, the developed framework can be applied to a broad range of multiobject multisurface segmentation problems.

Accuracy of 3D cartilage models generated from MR images is dependent on cartilage thickness: laser scanner based validation of in vivo cartilage

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 were overestimated in thinner (<2.5 mm) regions. The cartilage thicker than 2.5 mm was accurately predicted in MRI, though the thick cartilage in the central regions was underestimated. The accuracy of thickness measurements in the MRI-derived cartilage models systemically varied according to native cartilage thickness.

Acetabular cartilage thickness: accuracy of three-dimensional reconstructions from multidetector CT arthrograms in a cadaver study

PURPOSE

To prospectively quantify the accuracy of hip cartilage thickness estimated from three-dimensional (3D) surfaces, generated by segmenting multidetector computed tomographic (CT) arthrograms by using direct physical measurements of cartilage thickness as the reference standard.

MATERIALS AND METHODS

Four fresh-frozen cadaver hip joints from two male donors, ages 43 and 46 years, were obtained; institutional review board approval for cadaver research was also obtained. Sixteen holes were drilled perpendicular to the cartilage of four cadaveric acetabula (two specimens). Hip capsules were surgically closed, injected with contrast material, and scanned by using multidetector CT. After scanning, 5.3-mmcores were harvested concentrically at each drill hole and cartilage thickness was measured with a microscope. Cartilage was reconstructed in 3D by using commercial software. Segmentations were repeated by two authors. Reconstructed cartilage thickness was determined by using a published algorithm. Bland-Altman plots and linear regression were used to assess accuracy. Repeatability was quantified by using the coefficient of variation, intraclass correlation coefficient (ICC), repeatability coefficient, and percentage variability.

RESULTS

Cartilage was reconstructed to a bias of -0.13 mm and a repeatability coefficient of + or - 0.46 mm. Regression of the scatterplots indicated a tendency for multidetector CT to overestimate thickness. Intra- and interobserver repeatability were very good. For intraobserver correlation, the coefficient of variation was 14.80%, the ICC was 0.88, the repeatability coefficient was 0.55 mm, and the percentage variability was 11.77%. For interobserver correlation, the coefficient of variation was 13.47%, the ICC was 0.90, the repeatability coefficient was 0.52 mm, and the percentage variability was 11.63%.

CONCLUSION

Assuming that an accuracy of approximately + or - 0.5 mm is sufficient, reconstructions of cartilage geometry from multidetector CT arthrographic data could be used as a preoperative surgical planning tool.

Building 3-D statistical shape models by direct optimization

Statistical shape models are powerful tools for image interpretation and shape analysis. A simple, yet effective, way of building such models is to capture the statistics of sampled point coordinates over a training set of example shapes. However, a major drawback of this approach is the need to establish a correspondence across the training set. In 2-D, a correspondence is often defined using a set of manually placed 'landmarks' and linear interpolation to sample the shape in between. Such annotation is, however, time-consuming and subjective, particularly when extended to 3-D. In this paper, we show that it is possible to establish a dense correspondence across the whole training set automatically by treating correspondence as an optimization problem. The objective function we use for the optimization is based on the minimum description length principle, which we argue is a criterion that leads to models with good compactness, specificity, and generalization ability. We manipulate correspondence by reparameterizing each training shape. We describe an explicit representation of reparameterization for surfaces in 3-D that makes it impossible to generate an illegal (i.e., not one-to-one) correspondence. We also describe several large-scale optimization strategies for model building, and perform a detailed analysis of each approach. Finally, we derive quantitative measures of model quality, allowing meaningful comparison between models built using different methods. Results are given for several different training sets of 3-D shapes, which show that the minimum description length models perform significantly better than other approaches.

Magnetic resonance imaging-based semiquantitative and quantitative assessment in osteoarthritis

Whole organ magnetic resonance imaging (MRI)-based semiquantitative (SQ) assessment of knee osteoarthritis (OA), based on reliable scoring methods and expert reading, has become a powerful research tool in OA. SQ morphologic scoring has been applied to large observational cross-sectional and longitudinal epidemiologic studies as well as interventional clinical trials. SQ whole organ scoring analyzes all joint structures that are potentially relevant as surrogate outcome measures of OA and potential disease modification, including cartilage, subchondral bone, osteophytes, intra- and periarticular ligaments, menisci, synovial lining, cysts, and bursae. Resources needed for SQ scoring rely on the MRI protocol, image quality, experience of the expert readers, method of documentation, and the individual scoring system that will be applied. The first part of this article discusses the different available OA whole organ scoring systems, focusing on MRI of the knee, and also reviews alternative approaches. Rheumatologists are made aware of artifacts and differential diagnoses when applying any of the SQ scoring systems. The second part focuses on quantitative approaches in OA, particularly measurement of (subregional) cartilage loss. This approach allows one to determine minute changes that occur relatively homogeneously across cartilage structures and that are not apparent to the naked eye. To this end, the cartilage surfaces need to be segmented by trained users using specialized software. Measurements of knee cartilage loss based on water-excitation spoiled gradient recalled echo acquisition in the steady state, fast low-angle shot, or double-echo steady-state imaging sequences reported a 1% to 2% decrease in cartilage thickness annually, and a high degree of spatial heterogeneity of cartilage thickness changes in femorotibial subregions between subjects. Risk factors identified by quantitative measurement technology included a high body mass index, meniscal extrusion and meniscal tears, knee malalignment, advanced radiographic OA grade, bone marrow alterations, and focal cartilage lesions.

Computer input devices: neutral party or source of significant error in manual lesion segmentation?

Lesion segmentation involves outlining the contour of an abnormality on an image to distinguish boundaries between normal and abnormal tissue and is essential to track malignant and benign disease in medical imaging for clinical, research, and treatment purposes. A laser optical mouse and a graphics tablet were used by radiologists to segment 12 simulated reference lesions per subject in two groups (one group comprised three lesion morphologies in two sizes, one for each input device for each device two sets of six, composed of three morphologies in two sizes each). Time for segmentation was recorded. Subjects completed an opinion survey following segmentation. Error in contour segmentation was calculated using root mean square error. Error in area of segmentation was calculated compared to the reference lesion. 11 radiologists segmented a total of 132 simulated lesions. Overall error in contour segmentation was less with the graphics tablet than with the mouse (P < 0.0001). Error in area of segmentation was not significantly different between the tablet and the mouse (P = 0.62). Time for segmentation was less with the tablet than the mouse (P = 0.011). All subjects preferred the graphics tablet for future segmentation (P = 0.011) and felt subjectively that the tablet was faster, easier, and more accurate (P = 0.0005). For purposes in which accuracy in contour of lesion segmentation is of the greater importance, the graphics tablet is superior to the mouse in accuracy with a small speed benefit. For purposes in which accuracy of area of lesion segmentation is of greater importance, the graphics tablet and mouse are equally accurate.

Anatomically anchored template-based level set segmentation: application to quadriceps muscles in MR images from the Osteoarthritis Initiative

In this paper, we present a semi-automated segmentation method for magnetic resonance images of the quadriceps muscles. Our method uses an anatomically anchored, template-based initialization of the level set-based segmentation approach. The method only requires the input of a single point from the user inside the rectus femoris. The templates are quantitatively selected from a set of images based on modes in the patient population, namely, sex and body type. For a given image to be segmented, a template is selected based on the smallest Kullback-Leibler divergence between the histograms of that image and the set of templates. The chosen template is then employed as an initialization for a level set segmentation, which captures individual anatomical variations in the image to be segmented. Images from 103 subjects were analyzed using the developed method. The algorithm was trained on a randomly selected subset of 50 subjects (25 men and 25 women) and tested on the remaining 53 subjects. The performance of the algorithm on the test set was compared against the ground truth using the Zijdenbos similarity index (ZSI). The average ZSI means and standard deviations against two different manual readers were as follows: rectus femoris, 0.78 ± 0.12; vastus intermedius, 0.79 ± 0.10; vastus lateralis, 0.82 ± 0.08; and vastus medialis, 0.69 ± 0.16.

Intra- and inter-observer reproducibility of volume measurement of knee cartilage segmented from the OAI MR image set using a novel semi-automated segmentation method

OBJECTIVE

We developed a semi-automated method based on a graph-cuts algorithm for segmentation and volumetric measurements of the cartilage from high-resolution knee magnetic resonance (MR) images from the Osteoarthritis Initiative (OAI) database and assessed the intra- and inter-observer reproducibility of measurements obtained via this method.

DESIGN

MR image sets from 20 subjects of varying Kellgren-Lawrence (KL) grades (from 0 to IV) on fixed flexion knee radiographs were selected from the baseline double-echo and steady-state (DESS) knee MR images in the OAI database (0.B.1 Imaging Data set). Two trained radiologists independently performed the segmentation of knee cartilage twice using the semi-automated method. The volumes of segmented cartilage were computed and compared. The intra- and inter-observer reproducibility were determined by means of the coefficient of variation (CV%) of repeated cartilage segmented volume measurements. The subjects were also divided into the low- (0, I or II) and high-KL (III or IV) groups. The differences in cartilage volume measurements and CV% within and between the observers were tested with t tests.

RESULTS

The mean (+/-SD) intra-observer CV% for the 20 cases was 1.29 (+/-1.05)% for observer 1 and 1.67 (+/-1.14)% for observer 2, while the mean (+/-SD) inter-observer CV% was 1.31 (+/-1.26)% for session 1 and 1.79 (+/-1.72)% for session 2. There was no significant difference between the two intra-observer CV%'s (P=0.272) and between the two inter-observer CV%'s (P=0.353). The mean intra-observer CV% of the low-KL group was significantly smaller than that for the high-KL group for observer 1 (0.83 vs 1.86%: P=0.025). The segmentation processing times used by the two observers were significantly different (observer 1 vs 2): (mean 49+/-12 vs 33+/-6min) for session 1 and (49+/-8 vs 32+/-8min) for session 2.

CONCLUSION

The semi-automated graph-cuts method allowed us to segment and measure cartilage from high-resolution 3T MR images of the knee with high intra- and inter-observer reproducibility in subjects with varying severity of OA.

Magnetic resonance image segmentation using semi-automated software for quantification of knee articular cartilage---initial evaluation of a technique for paired scans

PURPOSE

Software-based image analysis is important for studies of cartilage changes in knee osteoarthritis (OA). This study describes an evaluation of a semi-automated cartilage segmentation software tool capable of quantifying paired images for potential use in longitudinal studies of knee OA. We describe the methodology behind the analysis and demonstrate its use by determination of test-retest analysis precision of duplicate knee magnetic resonance imaging (MRI) data sets.

METHODS

Test-retest knee MR images of 12 subjects with a range of knee health were evaluated from the Osteoarthritis Initiative (OAI) pilot MR study. Each subject was removed from the magnet between the two scans. The 3D DESS (sagittal, 0.456 mm x 0.365 mm, 0.7 mm slice thickness, TR 16.5 ms, TE 4.7 ms) images were obtained on a 3-T Siemens Trio MR system with a USA Instruments quadrature transmit-receive extremity coil. Segmentation of one 3D-image series was first performed and then the corresponding retest series was segmented by viewing both image series concurrently in two adjacent windows. After manual registration of the series, the first segmentation cartilage outline served as an initial estimate for the second segmentation. We evaluated morphometric measures of the bone and cartilage surface area (tAB and AC), cartilage volume (VC), and mean thickness (ThC.me) for medial/lateral tibia (MT/LT), total femur (F) and patella (P). Test-retest reproducibility was assessed using the root-mean square coefficient of variation (RMS CV%).

RESULTS

For the paired analyses, RMS CV % ranged from 0.9% to 1.2% for VC, from 0.3% to 0.7% for AC, from 0.6% to 2.7% for tAB and 0.8% to 1.5% for ThC.me.

CONCLUSION

Paired image analysis improved the measurement precision of cartilage segmentation. Our results are in agreement with other publications supporting the use of paired analysis for longitudinal studies of knee OA.

Oriented active shape models

Active shape models (ASM) are widely employed for recognizing anatomic structures and for delineating them in medical images. In this paper, a novel strategy called oriented active shape models (OASM) is presented in an attempt to overcome the following five limitations of ASM: 1) lower delineation accuracy, 2) the requirement of a large number of landmarks, 3) sensitivity to search range, 4) sensitivity to initialization, and 5) inability to fully exploit the specific information present in the given image to be segmented. OASM effectively combines the rich statistical shape information embodied in ASM with the boundary orientedness property and the globally optimal delineation capability of the live wire methodology of boundary segmentation. The latter characteristics allow live wire to effectively separate an object boundary from other nonobject boundaries with similar properties especially when they come very close in the image domain. The approach leads to a two-level dynamic programming method, wherein the first level corresponds to boundary recognition and the second level corresponds to boundary delineation, and to an effective automatic initialization method. The method outputs a globally optimal boundary that agrees with the shape model if the recognition step is successful in bringing the model close to the boundary in the image. Extensive evaluation experiments have been conducted by utilizing 40 image (magnetic resonance and computed tomography) data sets in each of five different application areas for segmenting breast, liver, bones of the foot, and cervical vertebrae of the spine. Comparisons are made between OASM and ASM based on precision, accuracy, and efficiency of segmentation. Accuracy is assessed using both region-based false positive and false negative measures and boundary-based distance measures. The results indicate the following: 1) The accuracy of segmentation via OASM is considerably better than that of ASM; 2) The number of landmarks can be reduced by a factor of 3 in OASM over that in ASM; 3) OASM becomes largely independent of search range and initialization becomes automatic. All three benefits of OASM ensue mainly from the severe constraints brought in by the boundary-orientedness property of live wire and the globally optimal solution found by the 2-level dynamic programming algorithm.

Quantitative imaging of musculoskeletal tissue

Quantitative imaging of musculoskeletal tissue, including radiography, computed tomography (CT), and magnetic resonance imaging (MRI), has become the essential methodology in clinical practice for diagnosis and monitoring of various musculoskeletal conditions. Furthermore, quantitative imaging technologies have become indispensable for research and development in diseases of the human skeleton. Standardized methods of image analysis have been developed through the years to quantify measurements on bone and cartilage with high precision and accuracy. Key areas of musculoskeletal disease where quantitative imaging is currently employed are osteoporosis and arthritis.

Quantitative MR imaging using "LiveWire" to measure tibiofemoral articular cartilage thickness

OBJECTIVE

To assess the reliability and accuracy of manual and semi-automated segmentation methods for quantifying knee cartilage thickness. This study employed both manual and LiveWire-based semi-automated segmentation methods, ex vivo and in vivo, to measure tibiofemoral (TF) cartilage thickness.

METHODS

The articular cartilage of a cadaver knee and a healthy volunteer's knee were segmented manually and with LiveWire from multiple 3T MR images. The cadaver specimen's cartilage thickness was also evaluated with a 3D laser scanner, which was assumed to be the gold standard. Thickness measurements were made within specific cartilage regions. The reliability of each segmentation method was assessed both ex vivo and in vivo, and accuracy was assessed ex vivo by comparing segmentation results to those obtained with laser scanning.

RESULTS

The cadaver specimen thickness measurements showed mean coefficients of variation (CVs) of 4.16%, 3.02%, and 1.59%, when evaluated with manual segmentation, LiveWire segmentation, and laser scanning, respectively. The cadaver specimen showed mean absolute errors versus laser scanning of 4.07% and 7.46% for manual and LiveWire segmentation, respectively. In vivo thickness measurements showed mean CVs of 2.71% and 3.65% when segmented manually and with LiveWire, respectively.

CONCLUSIONS

Manual segmentation, LiveWire segmentation, and laser scanning are repeatable methods for quantifying knee cartilage thickness; however, the measurements are technique-dependent. Ex vivo, the manual segmentation error was distributed around the laser scanning mean, while LiveWire consistently underestimated laser scanning by 8.9%. Although LiveWire offers repeatability and decreased segmentation time, manual segmentation more closely approximates true cartilage thickness, particularly in cartilage contact regions.

Measurement of knee cartilage thickness using MRI: a reproducibility study in a meniscectomized guinea pig model of osteoarthritis

The in vivo precision (reproducibility) of quantitative MRI is of particular importance in osteoarthritis (OA) progression of small magnitude and response to therapy. In this study, three-dimensional high-resolution MRI performed at 7 T was used to assess the short-term reproducibility of measurements of mean tibial cartilage thickness in a meniscectomized guinea pig model of OA. MR image acquisition was repeated five times in nine controls (SHAM) and 10 osteoarthritic animals 3 months after meniscectomy (MNX), in vivo. The animals were then killed for histomorphometric assessment and correlation with the MRI-based measurements. Medial tibial cartilage thickness was measured on MR images using semi-automatic dedicated 3D software developed in-house. The reproducibility of measurements of cartilage thickness was assessed by five repeated MRI examinations with a short recovery delay between examinations (48 h). The computed coefficients of variation were 8.9% for the SHAM group and 8.2% for the MNX group. The coefficients of variation were compatible with expected thickness variations between normal and pathological animals. A positive agreement and significant partial correlation (Spearman r' = 0.74; P < 0.01) between the MRI and histomorphometric data was established. Three-dimensional high-resolution MRI is a promising non-invasive research tool for in vivo follow-up. This modality could be used for staging and monitoring therapy response in small-animal models of OA.

Osteoarthritic cartilage is more homogeneous than healthy cartilage: identification of a superior region of interest colocalized with a major risk factor for osteoarthritis

RATIONALE AND OBJECTIVES

Cartilage loss as determined by magnetic resonance imaging (MRI) or joint space narrowing as determined by x-ray is the result of cartilage erosion. However, metabolic processes within the cartilage that later result in cartilage loss may be a more sensitive assessment method for early changes. Recently, it was shown that cartilage homogeneity visualized by MRI representing the biochemical changes undergoing in the cartilage is a potential marker for early detection of knee osteoarthritis (OA) and is also able to significantly separate groups of healthy subjects from those with OA. The purpose of this study was twofold. First, we wished to evaluate whether the results on cartilage homogeneity from the previous study can be reproduced using an independent population. Second, based on the homogeneity framework, we present an automatic technique that partitions the region of interest in the cartilage that contributes most to discrimination between healthy and OA subjects and allows for identification of the most implicated areas in early OA. These findings may allow further investigation of whether cartilage homogeneity reveals a predisposition for OA or whether it evolves as a consequence to disease and thereby can be used as a progression biomarker.

MATERIALS AND METHODS

A total of 283 right and left knees from 159 subjects aged 21 to 81 years were scanned using a Turbo 3D T1 sequence on a 0.18-T MRI Esaote scanner. The medial compartment of the tibial cartilage sheet was segmented using a fully automatic voxel classification scheme based on supervised learning. From the segmented cartilage sheet, homogeneity was quantified by measuring entropy from the distribution of signal intensities inside the compartment. Each knee was examined by radiography, and the knees were categorized by the Kellgren and Lawrence (KL) Index. Next, based on a gradient descent optimization technique, the cartilage region that contributed to the maximum statistical significance of homogeneity in separating healthy subjects from the diseased was partitioned. The generalizability of the region was evaluated by testing for overfitting. Three different regularization techniques were evaluated for reducing overfitting errors.

RESULTS

The P values for separating the different groups based on cartilage homogeneity were 2 x 10(-5) (KL 0 versus KL 1) and 1 x 10(-7) (KL 0 versus KL >0). Using the automatic gradient descent technique, the partitioned region was toward the peripheral part of the cartilage sheet. Using this region, the P values for separating the different groups based on homogeneity were 5 x 10(-9) (KL 0 versus KL 1) and 1 x 10(-15) (KL 0 versus KL >0). The precision of homogeneity for the partitioned region assessed as a test-retest root-mean-square coefficient of variation was 3.3%. Bootstrapping proved to be an effective regularization tool in reducing overfitting errors.

CONCLUSION

The validation study supported the use of cartilage homogeneity as a tool for the early detection of knee OA and for separating groups of healthy subjects from those who have disease. Our automatic, unbiased partitioning algorithm based on a general statistical framework outlined the cartilage region of interest that best separated healthy from OA conditions on the basis of homogeneity discrimination. We have shown that OA affects certain areas of the cartilage more distinctly, and these areas are located more toward the peripheral region of the cartilage. We propose that this region corresponds anatomically to cartilage covered by the meniscus in healthy subjects. This finding may provide valuable clues in the early detection and monitoring of OA and thus may improve treatment efficacy.

Separation of healthy and early osteoarthritis by automatic quantification of cartilage homogeneity

OBJECTIVE

Cartilage loss as determined either by magnetic resonance imaging (MRI) or by joint space narrowing in X-rays is the result of cartilage erosion. However, metabolic processes within the cartilage that later result in cartilage loss may be a more accurate assessment method for early changes. Early biological processes of cartilage destruction are among other things, a combination of proteoglycan turnover, as a result of altered charge distributions, and local alterations in water content (edema). As water distribution is detectable by MRI, the aim of this study was to investigate cartilage homogeneity visualized by MRI related to water distribution, as a potential very early marker for early detection of knee osteoarthritis (OA).

DESIGN

One hundred and fourteen right and left knees from 71 subjects aged 22-79 years were scanned using a Turbo 3D T(1) sequence on a 0.18T MRI Esaote scanner. The medial compartment of the tibial cartilage sheet was segmented using a fully automatic voxel classification scheme based on supervised learning. From the segmented cartilage sheet, homogeneity was quantified by measuring entropy from the distribution of signal intensities inside the compartment. For each knee an X-ray was acquired and the knees were categorized by the Kellgren and Lawrence (KL) index and the joint space width (JSW) was measured. The P-values for separating the groups by each of JSW, cartilage volume, cartilage mean intensity, and cartilage homogeneity were calculated using the unpaired t-test.

RESULTS

The P-value for separating the group diagnosed as KL 0 from the group being KL 1 based on JSW, volume and mean signal intensity the values were P=0.9, P=0.4 and P=0.0009, respectively. In contrast, the P-value for homogeneity was P=0.0004. The precision of the measures assessed, as a test-retest root mean square coefficient of variation (RMS-CV%) was 3.9% for JSW, 7.4% for volume, 3.9% for mean signal intensity and 3.0% for homogeneity quantification.

CONCLUSION

These data demonstrate that the distribution of components of the articular matrix precedes erosion, as measured by cartilage homogeneity related to water concentration. We show that homogeneity was able to separate early OA from healthy individuals in contrast to traditional volume and JSW quantifications. These data suggest that cartilage homogeneity quantification may be able to quantify early biochemical changes in articular cartilage prior to cartilage loss and thereby provide better identification of patients for OA trials who may respond better to medicinal intervention of some treatments. In addition, this study supports the feasibility of using low-field MRI in clinical studies.

Accuracy evaluation of automatic quantification of the articular cartilage surface curvature from MRI

RATIONALE AND OBJECTIVES

To study the articular cartilage surface curvature determined automatically from magnetic resonance (MR) knee scans, evaluate accuracy of the curvature estimates on digital phantoms, and an evaluation of their potential as disease markers for different stages of osteoarthritis (OA).

MATERIALS AND METHODS

Knee MR data were acquired using a low-field 0.18T scanner, along with posteroanterior x-rays for evaluation of radiographic signs of OA according to the Kellgren-Lawrence index (KL). Scans from a total of 114 knees from test subjects with KL 0-3, 59% females, ages 21-79 years were evaluated. The surface curvature for the medial tibial compartment was estimated automatically on a range of scales by two different methods: Euclidean shortening flow and boundary normal comparison on a cartilage shape model. The curvature estimates were normalized for joint size for intersubject comparisons. Digital phantoms were created to establish the accuracy of the curvature estimation methods.

RESULTS

A comparison of the two curvature estimation methods to ground truth yielded absolute pairwise differences of 1.1%, and 4.8%, respectively. The interscan reproducibility for the two methods were 2.3% and 6.4% (mean coefficient of variation), respectively. The surface curvature was significantly higher in the OA population (KL > 0) compared with the healthy population (KLi = 0) for both curvature estimates, with P values of .000004 and .000006, respectively. The shape model based curvature estimate could also separate healthy from borderline OA (KL = 1) populations (P = .005).

CONCLUSION

The phantom study showed that the shape model method was more accurate for a coarse-scale analysis, whereas the shortening flow estimated fine scales better. Both the fine- and the coarse-scale curvature estimates distinguished between healthy and OA populations, and the coarse-scale curvature could even distinguish between healthy and borderline OA populations. The highly significant differences between populations demonstrate the potential of cartilage curvature as a disease marker for OA.

Segmentation of articular cartilage using active contours and prior knowledge

A diffusion snake segmentation algorithm was evaluated on synthetic and real MR images of articular cartilage. The algorithm proved to be robust to missing boundaries and the initial contour converges over large distances. Compared with a standard B-spline snake, more accurate and reproducible segmentations were obtained, with less effort during initialization of the algorithm.

Novel fast semi-automated software to segment cartilage for knee MR acquisitions

OBJECTIVE

Validation of a new fast software technique to segment the cartilage on knee magnetic resonance (MR) acquisitions. Large studies of knee osteoarthritis (OA) will require fast and reproducible methods to quantify cartilage changes for knee MR data. In this report we document and measure the reproducibility and reader time of a software-based technique to quantify the volume and thickness of articular cartilage on knee MR images.

METHODS

The software was tested on a set of duplicate sagittal three-dimensional (3D) dual echo steady state (DESS) acquisitions from 15 (8 OA, 7 normal) subjects. The repositioning, inter-reader, and intra-reader reproducibility of the cartilage volume (VC) and thickness (ThC) were measured independently as well as the reader time for each cartilage plate. The root-mean square coefficient of variation (RMSCoV) was used as metric to quantify the reproducibility of VC and mean ThC.

RESULTS

The repositioning RMSCoV was as follows: VC=2.0% and ThC=1.2% (femur), VC=2.9% and ThC=1.6% (medial tibial plateau), VC=5.5% and ThC=2.4% (lateral tibial plateau), and VC=4.6% and ThC=2.3% (patella). RMSCoV values were higher for the inter-reader reproducibility (VC: 2.5-8.6%) (ThC: 1.9-5.2%) and lower for the intra-reader reproducibility (VC: 1.6-2.5%) (ThC: 1.2-1.9%). The method required an average of 75.4min per knee.

CONCLUSIONS

We have documented a fast reproducible semi-automated software method to segment articular cartilage on knee MR acquisitions.

Quantitative and topographical evaluation of ankle articular cartilage using high resolution MRI

The objectives of this study were to quantitatively evaluate the articular cartilage layers of the ankle and describe the cartilage topographical distribution across the joint surfaces using high resolution MRI and image segmentation. An anisotropic diffusion noise reduction algorithm and a directional gradient vector flow (dGVF) snake segmentation algorithm were applied to cartilage sensitive MR images. Eight cadaveric ankles were studied. Six repeated data sets were acquired in five of the ankles. Quantitative parameters were calculated for each cartilage layer; coefficients of variation (CV) were calculated from the six repeated data sets; and 3D thickness distribution maps were generated. The noise reduction algorithm produced marked image enhancement. Mean cartilage thickness ranged from 0.91 +/- 0.08 mm in the fibula to 1.34 +/- 0.14 mm in the talus. Mean cartilage volume was 3.32 +/- 0.55 ml, 1.72 +/- 0.25 ml, and 0.35 +/- 0.06 ml for the talus, tibia, and fibula, respectively. Mean CV ranged 2.82%-5.04% for quantitative parameters in the talus and tibia. The reported noise reduction and segmentation technique allow precise extraction of ankle cartilage and 3D reconstructions show that the thickest cartilage occurs over the talar shoulders, where osteochondritits dissecans (OCD) lesions commonly occur.