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Abstract #2482

Semi-automatic Prostate Segmentation via a Hidden Markov Model with Anatomical and Textural Priors

Christian Scharfenberger 1 , Dorothy Lui 1 , Farzad Khalvati 2 , Alexander Wong 1 , and Masoom Haider 2,3

1 Systems Design Engineering, University of Waterloo, Waterloo, Ontario, Canada, 2 Department of Medical Imaging, University of Toronto, Toronto, Ontario, Canada, 3 Sunnybrook Health Sciences Centre, Toronto, Ontario, Canada

The contouring and segmentation of the prostate gland is an important task in computer-aided prostate cancer screening using MRI. To assist medical professionals with the segmentation process, we propose a novel user-guided approach to prostate segmentation in MR images. The approach optimizes the energy components of a modified Decoupled Active Contour framework based on a Hidden Markov Model and a Rician likelihood to explicitly consider user guidance and textural and anatomical priors. Extensive experiments based on 10 patient cases and a variety of evaluation metrics showed that our approach provides a significant improvement over an existing semi-automatic segmentation approach.

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