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An Automatic Multi-atlas Segmentation of the Prostate in Transrectal Ultrasound Images Using Pairwise Atlas Shape Similarity
Saman Nouranian1, S. Sara Mahdavi1, Ingrid Spadinger2, William J. Morris2, Septimiu E. Salcudean1, and Purang Abolmaesumi1
1Department of Electrical and Computer Engineering, University of British Columbia, Vancouver, Canada
2Vancouver Cancer Center, British Columbia Cancer Agency, Vancouver, Canada
Abstract. 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.
LNCS 8150, p. 173 ff.
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© Springer-Verlag Berlin Heidelberg 2013