Abstract. Active Shape Models are a popular method for segmenting three-dimensional medical images. To obtain the required landmark correspondences, various automatic approaches have been proposed. In this work, we present an improved version of minimizing the description length (MDL) of the model. To initialize the algorithm, we describe a method to distribute landmarks on the training shapes using a conformal parameterization function. Next, we introduce a novel procedure to modify landmark positions locally without disturbing established correspondences. We employ a gradient descent optimization to minimize the MDL cost function, speeding up automatic model building by several orders of magnitude when compared to the original MDL approach. The necessary gradient information is estimated from a singular value decomposition, a more accurate technique to calculate the PCA than the commonly used eigendecomposition of the covariance matrix. Finally, we present results for several synthetic and real-world datasets demonstrating that our procedure generates models of significantly better quality in a fraction of the time needed by previous approaches.
Significant interscanner cartilage T2 differences were found and should be accounted for before data from scanners of different vendors are compared.
Magnetic resonance imaging (MRI) is emerging as the method of choice for measuring cartilage loss in osteoarthritis (OA), but current methods of analysis are imperfect for therapeutic clinical trials. In this paper, we present and evaluate, in two multicenter multivendor studies, a new method for anatomically corresponded regional analysis of cartilage (ACRAC) that allows analysis of knee cartilage morphology in anatomically corresponding focal regions defined on the bone surface. In our first study, 3-D knee MR Images were obtained from 19 asymptomatic female volunteers, followed by segmentations of the bone and cartilage. Minimum description length (MDL) statistical shape models (SSMs) were constructed from the segmented bone surfaces, providing mean bone shapes and a dense set of anatomically corresponding positions on each individual bone, the accuracy of which were measured using repeat images from a subset of the volunteers. Cartilage thicknesses were measured at these locations along 3-D normals to the bone surfaces, yielding corresponded cartilage thickness maps. Functional subregions of the joint were defined on the mean bone shapes, and propagated, using the correspondences, to each individual. ACRAC improved reproducibility, particularly in the central, load bearing subregions of the joint, compared with measures of volume obtained directly from the segmented cartilage surfaces. In our second study, MR Images were obtained from 31 female patient-volunteers with knee OA at baseline and six months. We obtained manual segmentations of the cartilage, and automatic segmentations of the bone using active appearance models (AAMs) built from the bone SSMs of the first study. ACRAC enabled the detection of significant thickness loss in the central, load-bearing regions of the whole femur (-5.57% p = 0.01, annualized) and the medial condyle (-13.08% , p = 0.024 Bonferroni corrected, annualized). We conclude that statistical shape modelling of bone surfaces defines correspondences invariant to individual joint size or shape, providing focal measures of cartilage with improved reproducibility compared to whole compartment measures. It permits the identification of anatomically equivalent regions, and provides the ability to identify the main load-bearing regions of the joint, based on the imputed premorbid state. The method permitted detection of tiny morphological change in cartilage thickness over six months in a small study, and may be useful for OA disease analysis and treatment monitoring.
ABSTRACT. We describe the application of a novel analysis method that provides detailed maps of changes in cartilage thickness measured from MRI scans for individuals and cohorts of patients together with regional measures. A cohort of osteoarthritis patients was imaged using a 1.0 T MR scanner over a 36-month period. Hyaline cartilage was manually segmented from a three-dimensional (3D) spoiled gradient-echo sequence with fat suppression. Representative outlines of the bone surfaces of the distal femur and proximal tibia were automatically generated from T 2 weighted images using statistical models of the shape and appearance of the bones. Cartilage thickness was measured from a dense set of points representing the bony surface. The models of the bones provided a common frame of reference, relative to which change maps were generated and aggregated across the cohort and anatomically corresponding subregions of the joint to be identified. In the reproducibility arm involving six patients, the thickness of cartilage had coefficients of variation of 2.66% within the tibiofemoral joint and 2.94% within the medial femoral condyle region. In the 9 patients (6 female, 3 male) who completed the 36-month study, the most striking observation was that lack of change in global measures of cartilage thickness concealed substantial focal changes. Specifically, the cartilage thickness within the tibiofemoral joint decreased by 0.85% per annum (95% CI 22.13% to 0.45%) with the medial femoral condyle as the region with the most significant change, decreasing by 2.43% per annum (uncorrected 95% CI 24.31% to 0.51%).
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