Semantic Context Forests for Learning-Based Knee Cartilage Segmentation in 3D MR Images

The automatic segmentation of human knee cartilage from 3D MR images is a useful yet challenging task due to the thin sheet structure of the cartilage with diffuse boundaries and inhomogeneous intensities. In this paper, we present an iterative multi-class learning method to segment the femoral, tibial and patellar cartilage simultaneously, which effectively exploits the spatial contextual constraints between bone and cartilage, and also between different cartilages. First, based on the fact that the cartilage grows in only certain area of the corresponding bone surface, we extract the distance features of not only to the surface of the bone, but more informatively, to the densely registered anatomical landmarks on the bone surface. Second, we introduce a set of iterative discriminative classifiers that at each iteration, probability comparison features are constructed from the class confidence maps derived by previously learned classifiers. These features automatically embed the semantic context information between different cartilages of interest. Validated on a total of 176 volumes from the Osteoarthritis Initiative (OAI) dataset, the proposed approach demonstrates high robustness and accuracy of segmentation in comparison with existing state-of-the-art MR cartilage segmentation methods.Comment: MICCAI 2013: Workshop on Medical Computer Visio

Early pre-radiographic structural pathology precedes the onset of accelerated knee osteoarthritis.

BACKGROUND: Accelerated knee osteoarthritis (AKOA) is characterized by more pain, impaired physical function, and greater likelihood to receive a joint replacement compared to individuals who develop the typical gradual onset of disease. Prognostic tools are needed to determine which structural pathologies precede the development of AKOA compared to individuals without AKOA. Therefore, the purpose of this manuscript was to determine which pre-radiographic structural features precede the development of AKOA. METHODS: The sample comprised participants in the Osteoarthritis Initiative (OAI) who had at least one radiographically normal knee at baseline (Kellgren-Lawrence [KL] grade  3) and No AKOA. The index visit was the study visit when participants met criteria for AKOA or a matched timepoint for those who did not develop AKOA. Magnetic resonance (MR) images were assessed for 12 structural features at the OAI baseline, and 1 and 2 years prior to the index visit. Separate logistic regression models (i.e. OAI baseline, 1 and 2 years prior) were used to determine which pre-radiographic structural features were more likely to antedate the development of AKOA compared to individuals not developing AKOA. RESULTS: At the OAI baseline visit, degenerative cruciate ligaments (Odds Ratio [OR] = 2.2, 95% Confidence Interval [CI] = 1.3,3.5), infrapatellar fat pad signal intensity alteration (OR = 2.0, 95%CI = 1.2,3.2), medial/lateral meniscal pathology (OR = 2.1/2.4, 95%CI = 1.3,3.4/1.5,3.8), and greater quantitative knee effusion-synovitis (OR = 2.2, 95%CI = 1.4,3.4) were more likely to antedate the development of AKOA when compared to those that did not develop AKOA. These results were similar at one and two years prior to disease onset. Additionally, medial meniscus extrusion at one year prior to disease onset (OR = 3.5, 95%CI = 2.1,6.0) increased the likelihood of developing AKOA. CONCLUSIONS: Early ligamentous degeneration, effusion/synovitis, and meniscal pathology precede the onset of AKOA and may be prognostic biomarkers

Composite quantitative knee structure metrics predict the development of accelerated knee osteoarthritis:data from the osteoarthritis initiative

BACKGROUND: We aimed to determine if composite structural measures of knee osteoarthritis (KOA) progression on magnetic resonance (MR) imaging can predict the radiographic onset of accelerated knee osteoarthritis. METHODS: We used data from a nested case-control study among participants from the Osteoarthritis Initiative without radiographic KOA at baseline. Participants were separated into three groups based on radiographic disease progression over 4 years: 1) accelerated (Kellgren-Lawrence grades [KL] 0/1 to 3/4), 2) typical (increase in KL, excluding accelerated osteoarthritis), or 3) no KOA (no change in KL). We assessed tibiofemoral cartilage damage (four regions: medial/lateral tibia/femur), bone marrow lesion (BML) volume (four regions: medial/lateral tibia/femur), and whole knee effusion-synovitis volume on 3 T MR images with semi-automated programs. We calculated two MR-based composite scores. Cumulative damage was the sum of standardized cartilage damage. Disease activity was the sum of standardized volumes of effusion-synovitis and BMLs. We focused on annual images from 2 years before to 2 years after radiographic onset (or a matched time for those without knee osteoarthritis). To determine between group differences in the composite metrics at all time points, we used generalized linear mixed models with group (3 levels) and time (up to 5 levels). For our prognostic analysis, we used multinomial logistic regression models to determine if one-year worsening in each composite metric change associated with future accelerated knee osteoarthritis (odds ratios [OR] based on units of 1 standard deviation of change). RESULTS: Prior to disease onset, the accelerated KOA group had greater average disease activity compared to the typical and no KOA groups and this persisted up to 2 years after disease onset. During a pre-radiographic disease period, the odds of developing accelerated KOA were greater in people with worsening disease activity [versus typical KOA OR (95% confidence interval [CI]): 1.58 (1.08 to 2.33); versus no KOA: 2.39 (1.55 to 3.71)] or cumulative damage [versus typical KOA: 1.69 (1.14 to 2.51); versus no KOA: 2.11 (1.41 to 3.16)]. CONCLUSIONS: MR-based disease activity and cumulative damage metrics may be prognostic markers to help identify people at risk for accelerated onset and progression of knee osteoarthritis

Diseased Region Detection of Longitudinal Knee Magnetic Resonance Imaging Data

Magnetic resonance imaging (MRI) has become an important imaging technique for quantifying the spatial location and magnitude/direction of longitudinal cartilage morphology changes in patients with osteoarthritis (OA). Although several analytical methods, such as subregion-based analysis, have been developed to refine and improve quantitative cartilage analyses, they can be suboptimal due to two major issues: the lack of spatial correspondence across subjects and time and the spatial heterogeneity of cartilage progression across subjects. The aim of this paper is to present a statistical method for longitudinal cartilage quantification in OA patients, while addressing these two issues. The 3D knee image data is preprocessed to establish spatial correspondence across subjects and/or time. Then, a Gaussian hidden Markov model (GHMM) is proposed to deal with the spatial heterogeneity of cartilage progression across both time and OA subjects. To estimate unknown parameters in GHMM, we employ a pseudo-likelihood function and optimize it by using an expectation-maximization (EM) algorithm. The proposed model can effectively detect diseased regions in each OA subject and present a localized analysis of longitudinal cartilage thickness within each latent subpopulation. Our GHMM integrates the strengths of two standard statistical methods including the local subregion-based analysis and the ordered value approach. We use simulation studies and the Pfizer longitudinal knee MRI dataset to evaluate the finite sample performance of GHMM in the quantification of longitudinal cartilage morphology changes. Our results indicate that GHMM significantly outperforms several standard analytical methods

A diagnostic imaging technique and therapeutic strategy for early osteoarthritis

Thesis (Ph.D.)--Boston UniversityOsteoarthritis (OA) is a chronic, progressive disease of diarthrodial joints arising from the breakdown of articular cartilage. As one of the leading causes of disability and lifestyle limitations in the United States, osteoarthritis is estimated to affect 27 million people in the U.S. and cost the economy $128 billion annually. Current diagnostic techniques detect OA only in its later stages, when irreversible cartilage damage has already occurred. A reliable, non-invasive method for diagnosing OA in its early stages would provide an opportunity to intervene and potentially to stay disease progression. Likewise, the field of OA research would benefit from a technique that allows tissue engineering and small molecule therapies to be evaluated longitudinally. Contrast-enhanced computed tomography (CECT) of cartilage is a developing medical imaging technique for evaluating cartilage biochemical and biomechanical properties. CECT has been shown to accurately quantify measures of cartilage integrity such as glycosaminoglycan (GAG) content, equilibrium compressive modulus, and coefficients of friction. In the studies presented herein, cationic iodinated contrast agents are developed for quantitative cartilage CECT, a technique predicated on the diffusion and partitioning of a charged contrast agent into the cartilage. The experiments show that cationic contrast agents lack specific interactions with anionic GAGs and are highly taken up in cartilage due, instead, to their electrostatic attraction. At diffusion equilibrium, both anionic and cationic agents indicate GAG content and biomechanical properties as measured by microcomputed tomography, though cationic contrast agents were found to diffuse through cartilage more slowly than anionic ones. Translating CECT to intact joints with clinically available helical CT scanners bears promising results, but concerns remain regarding in vivo applicability. Anionic contrast agents enable GAG content quantification following brief contrast agent exposure, whereas cationic agents require full equilibration within the tissue. To explore treatment modalities for early OA, a novel interpenetrating hydrogel method was developed to reconstitute the mechanical properties of cartilage models for early OA. Preliminary results show that the interpenetrating network strengthened cartilage with respect to compressive loading suggesting that the treatment could potentially serve as a functional replacement for GAG lost in the early stages of OA