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Tummala S.,Copenhagen University | Dam E.B.,Nordic Bioscience Imaging
Progress in Biomedical Optics and Imaging - Proceedings of SPIE | Year: 2010

Fully automatic imaging biomarkers may allow quantification of patho-physiological processes that a radiologist would not be able to assess reliably. This can introduce new insight but is problematic to validate due to lack of meaningful ground truth expert measurements. Rather than quantification accuracy, such novel markers must therefore be validated against clinically meaningful end-goals such as the ability to allow correct diagnosis. We present a method for automatic cartilage surface smoothness quantification in the knee joint. The quantification is based on a curvature flow method used on tibial and femoral cartilage compartments resulting from an automatic segmentation scheme. These smoothness estimates are validated for their ability to diagnose osteoarthritis and compared to smoothness estimates based on manual expert segmentations and to conventional cartilage volume quantification. We demonstrate that the fully automatic markers eliminate the time required for radiologist annotations, and in addition provide a diagnostic marker superior to the evaluated semi-manual markers. © 2010 Copyright SPIE - The International Society for Optical Engineering. Source


Tummala S.,Copenhagen University | Bay-Jensen A.-C.,Nordic Bioscience | Karsdal M.A.,Nordic Bioscience | Dam E.B.,Nordic Bioscience Imaging
Cartilage | Year: 2011

Objective: We investigated whether surface smoothness of articular cartilage in the medial tibiofemoral compartment quantified from magnetic resonance imaging (MRI) could be appropriate as a diagnostic marker of osteoarthritis (OA). Method: At baseline, 159 community-based subjects aged 21 to 81 with normal or OA-affected knees were recruited to provide a broad range of OA states. Smoothness was quantified using an automatic framework from low-field MRI in the tibial, femoral, and femoral subcompartments. Diagnostic ability of smoothness was evaluated by comparison with conventional OA markers, specifically cartilage volume from MRI, joint space width (JSW) from radiographs, and pain scores. Results: A total of 140 subjects concluded the 21-month study. Cartilage smoothness provided diagnostic ability in all compartments (P < 0.0001). The diagnostic smoothness markers performed at least similar to JSW and were superior to volume markers (e.g., the AUC for femoral smoothness of 0.80 was higher than the 0.57 for volume, P < 0.0001, and marginally higher than 0.73 for JSW, P = 0.25). The smoothness markers allowed diagnostic detection of pain presence (P < 0.05) and showed some correlation with pain severity (e.g., r = -0.32). The longitudinal change in smoothness was correlated with cartilage loss (r up to 0.60, P < 0.0001 in all compartments). Conclusions: This study demonstrated the potential of cartilage smoothness markers for diagnosis of moderate radiographic OA. Furthermore, correlations between smoothness and pain values and smoothness loss and cartilage loss supported a link to progression of OA. Thereby, smoothness markers may allow detection and monitoring of OA-supplemented currently accepted markers. © The Author(s) 2011. Source


Ganz M.,Copenhagen University | Nielsen M.,Copenhagen University | Brandt S.,Nordic Bioscience Imaging
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) | Year: 2010

We propose a statistical generative shape model for archipelago-like structures. These kind of structures occur, for instance, in medical images, where our intention is to model the appearance and shapes of calcifications in X-ray radio graphs. The generative model is constructed by (1) learning a patch-based dictionary for possible shapes, (2) building up a time-homogeneous Markov model to model the neighbourhood correlations between the patches, and (3) automatic selection of the model complexity by the minimum description length principle. The generative shape model is proposed as a probability distribution of a binary image where the model is intended to facilitate sequential simulation. Our results show that a relatively simple model is able to generate structures visually similar to calcifications. Furthermore, we used the shape model as a shape prior in the statistical segmentation of calcifications, where the area overlap with the ground truth shapes improved significantly compared to the case where the prior was not used. © 2010 Springer-Verlag Berlin Heidelberg. Source


Crimi A.,Copenhagen University | Lillholm M.,Nordic Bioscience Imaging | Nielsen M.,Copenhagen University | Ghosh A.,Trinity College Dublin | And 3 more authors.
IEEE Transactions on Medical Imaging | Year: 2011

The estimation of covariance matrices is a crucial step in several statistical tasks. Especially when using few samples of a high dimensional representation of shapes, the standard maximum likelihood estimation (ML) of the covariance matrix can be far from the truth, is often rank deficient, and may lead to unreliable results. In this paper, we discuss regularization by prior knowledge using maximum a posteriori (MAP) estimates. We compare ML to MAP using a number of priors and to Tikhonov regularization. We evaluate the covariance estimates on both synthetic and real data, and we analyze the estimates' influence on a missing-data reconstruction task, where high resolution vertebra and cartilage models are reconstructed from incomplete and lower dimensional representations. Our results demonstrate that our methods outperform the traditional ML method and Tikhonov regularization. © 2011 IEEE. Source


Qazi A.A.,Copenhagen University | Jorgensen D.R.,Copenhagen University | Lillholm M.,Nordic Bioscience Imaging | Loog M.,Technical University of Delft | And 2 more authors.
Medical Image Analysis | Year: 2010

We propose a fully automatic statistical framework for identifying the non-negative, real-valued weight map that best discriminate between two groups of objects. Given measurements on a spatially defined grid, a numerical optimization scheme is used to find the weight map that minimizes the sample size required to discriminate the two groups. The weight map produced by the method reflects the relative importance of the different areas in the objects, and the resulting sample size reduction is an important end goal in situations where data collection is difficult or expensive. An example is in clinical studies where the cost and the patient burden are directly related to the number of participants needed for the study. In addition, inspection of the weight map might provide clues that can lead to a better clinical understanding of the objects and pathologies being studied. The method is evaluated on synthetic data and on clinical data from knee cartilage MRI. The clinical data contain a total of 159 subjects aged 21-81 years and ranked from zero to four on the Kellgren-Lawrence osteoarthritis severity scale. Compared to a uniform weight map, we achieve sample size reductions up to 58% for cartilage thickness measurements. Based on quantifications from both morphometric and textural based imaging features, we also identify the most pathological areas in the articular cartilage. © 2010 Elsevier B.V. Source

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