Abstract
To simplify the trial-and-error process of adjusting objective function parameters (e.g. weights, dose limits) in prostate IMRT planning, we present a feasibility study showing that machine learning followed by a sensitivity-driven greedy search can quickly and automatically determine parameters that lead to a plan meeting the clinical requirements. The training database is composed of a large number of plans treated effectively under the 8640cGy prostate IMRT protocol. For each plan, the output features include the clinical setting of parameters, and the input features include simple dose statistics resulting from several fixed settings of parameters. We predict a new plan based on the 3 nearest plans in the training database that have similar input features. Starting from such a pre-plan, a sensitivity based automatic parameter search is applied to improve the plan's deficiencies. Experiments on a 39-patient dataset showed that a clinically acceptable (based on simplified dose calculation) prostate IMRT plan could be automatically determined within 2 minutes of optimization.
Keywords
Kernel principle, bladder, prostate, IMRT
Subject Categories
Diagnostic imaging
Disciplines
Engineering
Publisher
Bernard M. Gordon Center for Subsurface Sensing and Imaging Systems (Gordon-CenSSIS)
Publication Date
7-23-2007
Rights Holder
Bernard M. Gordon Center for Subsurface Sensing and Imaging Systems (Gordon-CenSSIS)
Permanent URL
Recommended Citation
Chen, Siqi, "Evaluation of an automatic algorithm based on Kernel Principal Component Analysis for segmentation of the bladder and prostate in CT scans" (2007). Research Thrust R2 Presentations. Paper 18. http://hdl.handle.net/2047/d10009048
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Notes
Poster presented at the 2007 Thrust R2D Image Understanding and Sensor Fusion Methods Conference