Abstract
In radiation therapy, multiatlas segmentation is recognized as being accurate, but is generally not considered scalable since the highest accuracy is achieved only when using a large atlas database. The fundamental problem is to use such a large database, to accurately represent the population variability, while conserving a relatively small computational cost. A method based on the composition of transformations is proposed to address this issue. The main novelties and key contributions of this paper are the definition of a transitivity error function and the presentation of an image clustering scheme that is based solely on the computed registration transformations. Leave-one-out experiments conducted on a database of \(N=50\) MR prostate scans demonstrate that a reduction of \((N-1)=49\)x in the number of pre-alignment registrations, and of 3.2x in term of total registration effort, is possible without significant impact on segmentation quality.
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Acknowledgments
This research was supported by the Cancer Council NSW (RG 11-05), the Prostate Cancer Foundation of Australia (YI2011), Movember and Cure Cancer Australia.
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Rivest-Hénault, D., Ghose, S., Pluim, J.P.W., Greer, P.B., Fripp, J., Dowling, J.A. (2014). Fast Multiatlas Selection Using Composition of Transformations for Radiation Therapy Planning. In: Menze, B., et al. Medical Computer Vision: Algorithms for Big Data. MCV 2014. Lecture Notes in Computer Science(), vol 8848. Springer, Cham. https://doi.org/10.1007/978-3-319-13972-2_10
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