The accurate delineation of anatomical structures is required in many medical image analysis applications. One example
is radiation therapy planning (RTP), where traditional manual delineation is tedious, labor intensive, and can require
hours of clinician's valuable time. Majority of automated segmentation methods in RTP belong to either model-based or
atlas-based approaches. One substantial limitation of model-based segmentation is that its accuracy may be restricted by
the uncertainties in image content, specifically when segmenting low-contrast anatomical structures, e.g. soft tissue
organs in computed tomography images. In this paper, we introduce a non-parametric feature enhancement filter which
replaces raw intensity image data by a high level probabilistic map which guides the deformable model to reliably
segment low-contrast regions. The method is evaluated by segmenting the submandibular and parotid glands in the head
and neck region and comparing the results to manual segmentations in terms of the volume overlap. Quantitative results
show that we are in overall good agreement with expert segmentations, achieving volume overlap of up to 80%.
Qualitatively, we demonstrate that we are able to segment low-contrast regions, which otherwise are difficult to delineate
with deformable models relying on distinct object boundaries from the original image data.
Osteoarthritis (OA) is a degenerative joint disease characterized by degradation of the articular cartilage, and is a
major cause of disability. At present, there is no cure for OA and currently available treatments are directed towards
relief of symptoms. Recently it was shown that cartilage homogeneity visualized by MRI and representing the
biochemical changes undergoing in the cartilage is a potential marker for early detection of knee OA. In this paper based
on homogeneity we present an automatic technique, embedded in a variational framework, for localization of a region of
interest in the knee cartilage that best indicates where the pathology of the disease is dominant. The technique is
evaluated on 283 knee MR scans. We show that OA affects certain areas of the cartilage more distinctly, and these are
more towards the peripheral region of the cartilage. We propose that this region in the cartilage corresponds anatomically
to the area covered by the meniscus in healthy subjects. This finding may provide valuable clues in the pathology and the
etiology of OA and thereby may improve treatment efficacy. Moreover our method is generic and may be applied to
other organs as well.
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.
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