TY - GEN
T1 - Fast multiatlas selection using composition of transformations for radiation therapy planning
AU - Rivest-Hénault, D.
AU - Ghose, S.
AU - Pluim, J.P.W.
AU - Greer, P.B.
AU - Fripp, J.
AU - Dowling, J.A.
PY - 2014
Y1 - 2014
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=84917728077&partnerID=8YFLogxK
U2 - 10.1007/978-3-319-13972-2_10
DO - 10.1007/978-3-319-13972-2_10
M3 - Conference contribution
AN - SCOPUS:84917728077
SN - 9783319139715
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 105
EP - 115
BT - Medical Computer Vision : Algorithms for Big Data
A2 - Menze, Bj.
A2 - Langs, G.
A2 - Montillo, A.
A2 - Kelm, M.
A2 - Mueller, H.
A2 - Zhang, Sh.
A2 - Cai, W.
A2 - Metaxas, D.
PB - Springer
CY - Berlin
T2 - MICCAI 2014 Workshop on Medical Computer Vision: Algorithms for Big Data (bigMCV 2014), September 18, 2014, Cambridge, MA, USA
Y2 - 18 September 2014 through 18 September 2014
ER -