Atlas-Based Whole-Body PET-CT Segmentation Using a Passive Contour Distance

Fabian Gigengack, Lars Ruthotto, Xiaoyi Jiang, Jan Modersitzki, Martin Burger, Sven Hermann, Klaus P. Schäfers


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 textpm 0.10 for the left ventricle, 0.88 textpm 0.05 for the bladder, and 0.76 textpm 0.07 for the kidneys shows the high accuracy of our method.
TitelMedical Computer Vision. Recognition Techniques and Applications in Medical Imaging
Redakteure/-innenBjoern H. Menze, Georg Langs, Le Lu, Albert Montillo, Zhuowen Tu, Antonio Criminisi
ErscheinungsortBerlin, Heidelberg
Herausgeber (Verlag)Springer Berlin Heidelberg
ISBN (Print)978-3-642-36619-2
ISBN (elektronisch)978-3-642-36620-8
PublikationsstatusVeröffentlicht - 10.2013
Veranstaltung2th International MICCAI Workshop on Medical Computer Vision - Nice, Frankreich
Dauer: 05.10.201205.10.2012


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