Statistical volumetric model for characterization and visualization of prostate cancer

Jianping Lu; Rujirutana Srikanchana; Maxine A. McClain; Yue Joseph Wang; Jian Hua Xuan; Isabell A. Sesterhenn; Matthew T. Freedman M.D.; Seong Ki Mun

doi:10.1117/12.383036

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18 April 2000 Statistical volumetric model for characterization and visualization of prostate cancer

Jianping Lu; Rujirutana Srikanchana; Maxine A. McClain; Yue Joseph Wang; Jian Hua Xuan; Isabell A. Sesterhenn; Matthew T. Freedman M.D.; Seong Ki Mun

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Proceedings Volume 3976, Medical Imaging 2000: Image Display and Visualization; (2000) https://doi.org/10.1117/12.383036
Event: Medical Imaging 2000, 2000, San Diego, CA, United States

Abstract

To reveal the spatial pattern of localized prostate cancer distribution, a 3D statistical volumetric model, showing the probability map of prostate cancer distribution, together with the anatomical structure of the prostate, has been developed from 90 digitally-imaged surgical specimens. Through an enhanced virtual environment with various visualization modes, this master model permits for the first time an accurate characterization and understanding of prostate cancer distribution patterns. The construction of the statistical volumetric model is characterized by mapping all of the individual models onto a generic prostate site model, in which a self-organizing scheme is used to decompose a group of contours representing multifold tumors into localized tumor elements. Next crucial step of creating the master model is the development of an accurate multi- object and non-rigid registration/warping scheme incorporating various variations among these individual moles in true 3D. This is achieved with a multi-object based principle-axis alignment followed by an affine transform, and further fine-tuned by a thin-plate spline interpolation driven by the surface based deformable warping dynamics. Based on the accurately mapped tumor distribution, a standard finite normal mixture is used to model the cancer volumetric distribution statistics, whose parameters are estimated using both the K-means and expectation- maximization algorithms under the information theoretic criteria. Given the desired number of tissue samplings, the prostate needle biopsy site selection is optimized through a probabilistic self-organizing map thus achieving a maximum likelihood of cancer detection. We describe the details of our theory and methodology, and report our pilot results and evaluation of the effectiveness of the algorithm in characterizing prostate cancer distributions and optimizing needle biopsy techniques.

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Jianping Lu, Rujirutana Srikanchana, Maxine A. McClain, Yue Joseph Wang, Jian Hua Xuan, Isabell A. Sesterhenn, Matthew T. Freedman M.D., and Seong Ki Mun "Statistical volumetric model for characterization and visualization of prostate cancer", Proc. SPIE 3976, Medical Imaging 2000: Image Display and Visualization, (18 April 2000); https://doi.org/10.1117/12.383036

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