Paper
9 May 2002 Diaphragm dome surface segmentation in CT data sets: a 3D active appearance model approach
Reinhard Beichel, Georg Gotschuli, Erich Sorantin, Franz W. Leberl, Milan Sonka
Author Affiliations +
Abstract
Knowledge about the location of the diaphragm dome surface, which separates the lungs and the heart from the abdominal cavity, is of vital importance for applications like automated segmentation of adjacent organs (e.g., liver) or functional analysis of the respiratory cycle. We present a new 3D Active Appearance Model (AAM) approach to segmentation of the top layer of the diaphragm dome. The 3D AAM consists of three parts: a 2D closed curve (reference curve), an elevation image and texture layers. The first two parts combined represent 3D shape information and the third part image intensity of the diaphragm dome and the surrounding layers. Differences in height between dome voxels and a reference plane are stored in the elevation image. The reference curve is generated by a parallel projection of the diaphragm dome outline in the axial direction. Landmark point placement is only done on the (2D) reference curve, which can be seen as the bounding curve of the elevation image. Matching is based on a gradient-descent optimization process and uses image intensity appearance around the actual dome shape. Results achieved in 60 computer generated phantom data sets show a high degree of accuracy (positioning error -0.07+/-1.29 mm). Validation using real CT data sets yielded a positioning error of -0.16+/-2.95 mm. Additional training and testing on in-vivo CT image data is ongoing.
© (2002) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Reinhard Beichel, Georg Gotschuli, Erich Sorantin, Franz W. Leberl, and Milan Sonka "Diaphragm dome surface segmentation in CT data sets: a 3D active appearance model approach", Proc. SPIE 4684, Medical Imaging 2002: Image Processing, (9 May 2002); https://doi.org/10.1117/12.467190
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Cited by 32 scholarly publications.
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KEYWORDS
3D modeling

Image segmentation

Data modeling

3D image processing

Liver

Heart

Image processing

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