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

Summary: Automatic Segmentation of the Pelvic Bones from CT Data
Based on a Statistical Shape Model
H. Seim1
, D. Kainmueller1
, M. Heller2
, H. Lamecker1
, S. Zachow1
, H.-C. Hege1
1Medical Planning Group, Zuse-Institute Berlin, Berlin, Germany
2Julius Wolff Institut and Center for Musculoskeletal Surgery Charité-Universitätsmedizin Berlin, Germany
We 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


Source: Andrzejak, Artur - Konrad-Zuse-Zentrum für Informationstechnik Berlin


Collections: Computer Technologies and Information Sciences