Abstract
In positron emission tomography (PET) imaging, the segmentation of organs is necessary for many quantitative image analysis tasks, e.g., estimation of individual organ concentration or partial volume correction. To this end we present a fully automated approach for wholebody segmentation which enables large-scale and reproducible studies. The approach is based on joint segmentation and atlas registration. The classical active contour approach by Chan and Vese is modified to a novel passive contour energy term with implicitly incorporated information about shape and location of the organs. This new energy is added to a registration functional which is based on both functional (PET) and morphological (CT) data. The proposed method is applied to medical data, given by 13 PET-CT data sets of mice, and quantitatively compared to manually drawn VOIs. An average Dice coefficient of 0.73 ± 0.10 for the left ventricle, 0.88 ± 0.05 for the bladder, and 0.76 ± 0.07 for the kidneys shows the high accuracy of our method.
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Gigengack, F. et al. (2013). Atlas-Based Whole-Body PET-CT Segmentation Using a Passive Contour Distance. In: Menze, B.H., Langs, G., Lu, L., Montillo, A., Tu, Z., Criminisi, A. (eds) Medical Computer Vision. Recognition Techniques and Applications in Medical Imaging. MCV 2012. Lecture Notes in Computer Science, vol 7766. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-36620-8_9
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DOI: https://doi.org/10.1007/978-3-642-36620-8_9
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