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21 March 2014 Failure analysis for model-based organ segmentation using outlier detection
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During the last years Model-Based Segmentation (MBS) techniques have been used in a broad range of medical applications. In clinical practice, such techniques are increasingly employed for diagnostic purposes and treatment decisions. However, it is not guaranteed that a segmentation algorithm will converge towards the desired solution. In specific situations as in the presence of rare anatomical variants (which cannot be represented) or for images with an extremely low quality, a meaningful segmentation might not be feasible. At the same time, an automated estimation of the segmentation reliability is commonly not available. In this paper we present an approach for the identification of segmentation failures using concepts from the field of outlier detection. The approach is validated on a comprehensive set of Computed Tomography Angiography (CTA) images by means of Receiver Operating Characteristic (ROC) analysis. Encouraging results in terms of an Area Under the ROC Curve (AUC) of up to 0.965 were achieved.
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Axel Saalbach, Irina Wächter Stehle, Cristian Lorenz, and Jürgen Weese "Failure analysis for model-based organ segmentation using outlier detection", Proc. SPIE 9034, Medical Imaging 2014: Image Processing, 903408 (21 March 2014);

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