Generating prostate models by means of geometric deformation with application to computerized training of cryosurgery - Abstract

PURPOSE:As a part of an ongoing project to develop computerized training tools for cryosurgery, the objective of the current study is twofold: to compile literature data on the likelihood of cancer tumor growth and its effect on the prostate shape and to present a deformation scheme for a 3D organ template in order to generate clinically relevant prostate models.

The long-term objective of this study is to develop a database of prostate models for computerized training.

METHODS:Cryosurgery is typically performed on patients with localized prostate cancer found in stage T3 or earlier. The distribution of key geometric features likely to be found in the prostate at stage T3 is integrated into a 3D prostate template by employing the extended free-form deformation (EFFD) method. The applied scheme combines two steps: pre-selecting a set of geometric parameter values and manipulating the lattice control points until the prostate model meets the desired criteria.

RESULTS:Examples for model generation are displayed, based on two 3D prostate templates previously obtained from ultrasound imaging. These examples include selected cases with unilateral and bilateral stage T3 tumor growth, suitable for incorporation into a training database.

CONCLUSIONS: EFFD is an efficient method for rapid generation of prostate models. The compiled criteria for model generation do not lead to a unique shape since the contours for template deformation are randomly selected. Nevertheless, these criteria do lead to shapes resembling cancer growth, as various growth histories can lead to a tumor characterized by the same key parameter values.

Written by:
Sehrawat A, Shimada K, Rabin Y.   Are you the author?
Department of Mechanical Engineering, Carnegie Mellon University, Pittsburgh, PA, 15213, USA.

Reference: Int J Comput Assist Radiol Surg. 2012 Jul 11. Epub ahead of print.
doi: 10.1007/s11548-012-0780-8


PubMed Abstract
PMID: 22782183

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