Delineation of the prostate from transrectal ultrasound images is a necessary step in several computer-assisted clinical interventions, such as low dose rate brachytherapy.
Current approaches to user segmentation require user intervention and therefore it is subject to user errors. It is desirable to have a fully automatic segmentation for improved segmentation consistency and speed. In this paper, we propose a multi-atlas fusion framework to automatically segment prostate transrectal ultrasound images. The framework initially registers a dataset of a priori segmented ultrasound images to a target image. Subsequently, it uses the pairwise similarity of registered prostate shapes, which is independent of the image-similarity metric optimized during the registration process, to prune the dataset prior to the fusion and consensus segmentation step. A leave-one-out cross-validation of the proposed framework on a dataset of 50 transrectal ultrasound volumes obtained from patients undergoing brachytherapy treatment shows that the proposed is clinically robust, accurate and reproducible.
Written by:
Nouranian S, Mahdavi SS, Spadinger I, Morris WJ, Salcudean SE, Abolmaesumi P. Are you the author?
Department of Electrical and Computer Engineering, University of British Columbia, Vancouver, Canada; Vancouver Cancer Center, British Columbia Cancer Agency, Vancouver, Canada.
Reference: Med Image Comput Comput Assist Interv. 2013;16(Pt 2):173-80.
PubMed Abstract
PMID: 24579138
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