Osteoarthritis (OA) is a degenerative joint disease characterized by articular cartilage degradation. A central problem in
clinical trials is quantification of progression and early detection of the disease. The accepted standard for evaluating OA
progression is to measure the joint space width from radiographs however; there the cartilage is not visible. Recently
cartilage volume and thickness measures from MRI are becoming popular, but these measures don't account for the
biochemical changes undergoing in the cartilage before cartilage loss even occurs and therefore are not optimal for early
detection of OA. As a first step, we quantify cartilage homogeneity (computed as the entropy of the MR intensities) from
114 automatically segmented medial compartments of tibial cartilage sheets from Turbo 3D T1 sequences, from subjects
with no, mild or severe OA symptoms. We show that homogeneity is a more sensitive technique than volume
quantification for detecting early OA and for separating healthy individuals from diseased. During OA certain areas of
the cartilage are affected more and it is believed that these are the load-bearing regions located at the center of the
cartilage. Based on the homogeneity framework we present an automatic technique that partitions the region on the
cartilage that contributes to maximum homogeneity discrimination. These regions however, are more towards the noncentral
regions of the cartilage. Our observation will provide valuable clues to OA research and may lead to improving
treatment efficacy.
KEYWORDS: Cartilage, Image segmentation, Magnetic resonance imaging, 3D image processing, Image processing, Image classification, 3D scanning, Basic research, Machine learning, Drug development
Accurate computation of the thickness of the articular cartilage is
of great importance when diagnosing and monitoring the progress of
joint diseases such as osteoarthritis. A fully automated cartilage
assessment method is preferable compared to methods using manual
interaction in order to avoid inter- and intra-observer variability.
As a first step in the cartilage assessment, we present an automatic
method for locating articular cartilage in knee MRI using supervised
learning. The next step will be to fit a variable shape model to the
cartilage, initiated at the location found using the method
presented in this paper. From the model, disease markers will be
extracted for the quantitative evaluation of the cartilage. The
cartilage is located using an ANN-classifier, where every voxel is
classified as cartilage or non-cartilage based on prior knowledge of
the cartilage structure. The classifier is tested using leave-one-out-evaluation, and we found the average sensitivity and specificity to be 91.0% and 99.4%, respectively. The center of mass calculated from voxels classified as cartilage are similar to the corresponding values calculated from manual segmentations, which confirms that this method can find a good initial position for a shape model.
An important question in mammographic image analysis is the importance of the projected view of the breast. Can temporal changes in density be detected equally well using either one of the commonly available views Medio-Lateral (ML) and Cranio-Caudal (CC) or a combination of the two? Two sets of mammograms of 50 patients in a double-blind, placebo controlled hormone replacement therapy (HRT) experiment were used. One set of ML and CC view from 1999 and one from 2001. HRT increases density which means that the degree of
separation of the populations (one group receiving HRT and the other placebo) can be used as a measure of how much density change information is carried in a particular view or combination of views. Earlier results have shown a high correlation between CC and ML views leading to the conclusion that only one of them is needed for density assessment purposes. A similar high correlation coefficient was observed in this study (0.85), while the correlation between changes was a bit lower (0.71). Using both views to separate the patients receiving hormones from the ones receiving placebo increased the area under corresponding ROC curves from 0.76 ± 0.04 to 0.79 ± 0.04.
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