Automatic Segmentation of the Pelvic Bones from CT Data Based on a Statistical Shape Model

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dc.contributor.authorSeim, Heikoen_US
dc.contributor.authorKainmueller, Dagmaren_US
dc.contributor.authorHeller, Markusen_US
dc.contributor.authorLamecker, Hansen_US
dc.contributor.authorZachow, Stefanen_US
dc.contributor.authorHege, Hans-Christianen_US
dc.contributor.editorCharl Botha and Gordon Kindlmann and Wiro Niessen and Bernhard Preimen_US
dc.date.accessioned2014-01-29T17:02:11Z
dc.date.available2014-01-29T17:02:11Z
dc.date.issued2008en_US
dc.description.abstractWe present an algorithm for automatic segmentation of the human pelvic bones from CT datasets that is based on the application of a statistical shape model. The proposed method is divided into three steps: 1) The averaged shape of the pelvis model is initially placed within the CT data using the Generalized Hough Transform, 2) the statistical shape model is then adapted to the image data by a transformation and variation of its shape modes, and 3) a final free-form deformation step based on optimal graph searching is applied to overcome the restrictive character of the statistical shape representation. We thoroughly evaluated the method on 50 manually segmented CT datasets by performing a leave-one-out study. The Generalized Hough Transform proved to be a reliable method for an automatic initial placement of the shape model within the CT data. Compared to the manual gold standard segmentations, our automatic segmentation approach produced an average surface distance of 1.2 ± 0.3mm after the adaptation of the statistical shape model, which could be reduced to 0.7±0.3mm using a final free-form deformation step. Together with an average segmentation time of less than 5 minutes, the results of our study indicate that our method meets the requirements of clinical routine.en_US
dc.description.seriesinformationEurographics Workshop on Visual Computing for Biomedicineen_US
dc.identifier.isbn978-3-905674-13-2en_US
dc.identifier.issn2070-5786en_US
dc.identifier.urihttps://doi.org/10.2312/VCBM/VCBM08/093-100en_US
dc.publisherThe Eurographics Associationen_US
dc.subjectCategories and Subject Descriptors (according to ACM CCS): I.4.9 [Image Processing and Computer Vision]: Applicationsen_US
dc.titleAutomatic Segmentation of the Pelvic Bones from CT Data Based on a Statistical Shape Modelen_US
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VCBM 08: Eurographics Workshop on Visual Computing for Biomedicine