- Title
- Fast multiatlas selection using composition of transformations for radiation therapy planning
- Creator
- Rivest-Hénault, David; Ghose, Soumya; Pluim, Josien P. W.; Greer, Peter B.; Fripp, Jurgen; Dowling, Jason A.
- Relation
- 2014 Workshop on Medical Computer Vision: Algorithms for Big Data (MCV 2014). Lecture Notes in Computer Science, Volume 8848 (Cambridge, MA 18-19 September, 2014) p. 105-115
- Publisher Link
- http://dx.doi.org/10.1007/978-3-319-13972-2_10
- Publisher
- Springer
- Resource Type
- conference paper
- Date
- 2014
- Description
- 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)=49x 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.
- Subject
- simulation and modeling; image processing; pattern recognition
- Identifier
- http://hdl.handle.net/1959.13/1297981
- Identifier
- uon:19545
- Identifier
- ISBN:9783319139715
- Language
- eng
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