A stochastic large deformation model for computational anatomy
Publikation: Bidrag til bog/antologi/rapport › Konferencebidrag i proceedings › Forskning › fagfællebedømt
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A stochastic large deformation model for computational anatomy. / Arnaudon, Alexis; Holm, Darryl D.; Pai, Akshay Sadananda Uppinakudru; Sommer, Stefan Horst.
Information Processing in Medical Imaging: 25th International Conference, IPMI 2017, Boone, NC, USA, June 25-30, 2017, Proceedings. Springer, 2017. s. 571-582 (Lecture notes in computer science, Bind 10265).Publikation: Bidrag til bog/antologi/rapport › Konferencebidrag i proceedings › Forskning › fagfællebedømt
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TY - GEN
T1 - A stochastic large deformation model for computational anatomy
AU - Arnaudon, Alexis
AU - Holm, Darryl D.
AU - Pai, Akshay Sadananda Uppinakudru
AU - Sommer, Stefan Horst
N1 - Conference code: 25
PY - 2017
Y1 - 2017
N2 - In the study of shapes of human organs using computational anatomy, variations are found to arise from inter-subject anatomical differences, disease-specific effects, and measurement noise. This paper introduces a stochastic model for incorporating random variations into the Large Deformation Diffeomorphic Metric Mapping (LDDMM) framework. By accounting for randomness in a particular setup which is crafted to fit the geometrical properties of LDDMM, we formulate the template estimation problem for landmarks with noise and give two methods for efficiently estimating the parameters of the noise fields from a prescribed data set. One method directly approximates the time evolution of the variance of each landmark by a finite set of differential equations, and the other is based on an Expectation-Maximisation algorithm. In the second method, the evaluation of the data likelihood is achieved without registering the landmarks, by applying bridge sampling using a stochastically perturbed version of the large deformation gradient flow algorithm. The method and the estimation algorithms are experimentally validated on synthetic examples and shape data of human corpora callosa.
AB - In the study of shapes of human organs using computational anatomy, variations are found to arise from inter-subject anatomical differences, disease-specific effects, and measurement noise. This paper introduces a stochastic model for incorporating random variations into the Large Deformation Diffeomorphic Metric Mapping (LDDMM) framework. By accounting for randomness in a particular setup which is crafted to fit the geometrical properties of LDDMM, we formulate the template estimation problem for landmarks with noise and give two methods for efficiently estimating the parameters of the noise fields from a prescribed data set. One method directly approximates the time evolution of the variance of each landmark by a finite set of differential equations, and the other is based on an Expectation-Maximisation algorithm. In the second method, the evaluation of the data likelihood is achieved without registering the landmarks, by applying bridge sampling using a stochastically perturbed version of the large deformation gradient flow algorithm. The method and the estimation algorithms are experimentally validated on synthetic examples and shape data of human corpora callosa.
KW - Computational anatomy
KW - Large deformations
KW - LDDMM
KW - Stochastic processes
UR - http://www.scopus.com/inward/record.url?scp=85020552205&partnerID=8YFLogxK
U2 - 10.1007/978-3-319-59050-9_45
DO - 10.1007/978-3-319-59050-9_45
M3 - Article in proceedings
AN - SCOPUS:85020552205
SN - 978-3-319-59049-3
T3 - Lecture notes in computer science
SP - 571
EP - 582
BT - Information Processing in Medical Imaging
PB - Springer
Y2 - 25 June 2017 through 30 June 2017
ER -
ID: 184143085