14 March 2008 Application of statistical cancer atlas for 3D biopsy
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Abstract
Prostate cancer is the most commonly diagnosed cancer in males in the United States and the second leading cause of cancer death. While the exact cause is still under investigation, researchers agree on certain risk factors like age, family history, dietary habits, lifestyle and race. It is also widely accepted that cancer distribution within the prostate is inhomogeneous, i.e. certain regions have a higher likelihood of developing cancer. In this regard extensive work has been done to study the distribution of cancer in order to perform biopsy more effectively. Recently a statistical cancer atlas of the prostate was demonstrated along with an optimal biopsy scheme achieving a high detection rate. In this paper we discuss the complete construction and application of such an atlas that can be used in a clinical setting to effectively target high cancer zones during biopsy. The method consists of integrating intensity statistics in the form of cancer probabilities at every voxel in the image with shape statistics of the prostate in order to quickly warp the atlas onto a subject ultrasound image. While the atlas surface can be registered to a pre-segmented subject prostate surface or instead used to perform segmentation of the capsule via optimization of shape parameters to segment the subject image, the strength of our approach lies in the fast mapping of cancer statistics onto the subject using shape statistics. The shape model was trained from over 38 expert segmented prostate surfaces and the atlas registration accuracy was found to be high suggesting the use of this method to perform biopsy in near real time situations with some optimization.
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Ramkrishnan Narayanan, Dinggang Shen, Christos Davatzikos, E. David Crawford, Albaha Barqawi, Priya Werahera, Dinesh Kumar, Jasjit S. Suri, "Application of statistical cancer atlas for 3D biopsy", Proc. SPIE 6812, Image Processing: Algorithms and Systems VI, 681216 (14 March 2008); doi: 10.1117/12.766633; https://doi.org/10.1117/12.766633
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