Abstract
This paper presents a Bayesian multi-scale three dimensional deformable template approach based on a medial representation for the segmentation and shape characterization of anatomical objects in medical imagery. Prior information about the geometry and shape of the anatomical objects under study is incorporated via the construction of exemplary templates. The anatomical variability is accommodated in the Bayesian framework by defining probabilistic transformations on these templates. The modeling approach taken in this paper for building exemplary templates and associated transformations is based on a multi-scale medial representation. The transformations defined in this framework are parameterized directly in terms of natural shape operations, such as thickening and bending, and their location. Quantitative validation results are presented on the automatic segmentation procedure developed for the extraction of the kidney parenchyma-including the renal pelvis-in subjects undergoing radiation treatment for cancer. We show that the segmentation procedure developed in this paper is efficient and accurate to within the voxel resolution of the imaging modality.
Acknowledgement
We thank Prof. Gerig and Matthieu Jomier for the use of their scoring tool for the comparison of segmentation as well as for the many insightful discussions and comments. We would like to also thank Dr. Zhi Chen for the generating the table comparing the segmentations. We also thank Prof. Ed. Chaney for providing us the data sets and invaluable insights. This work was supported by NIH Grants P01 CA47982 R01 CA67183 This research was carried out on computers donated by Intel.
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Joshi, S., Pizer, S., Fletcher, P.T., Thall, A., Tracton, G. (2001). Multi-scale 3-D Deformable Model Segmentation Based on Medial Description. In: Insana, M.F., Leahy, R.M. (eds) Information Processing in Medical Imaging. IPMI 2001. Lecture Notes in Computer Science, vol 2082. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45729-1_6
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DOI: https://doi.org/10.1007/3-540-45729-1_6
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