A stochastic large deformation model for computational anatomy

Publikation: Bidrag til bog/antologi/rapportKonferencebidrag i proceedingsForskningfagfællebedømt

Alexis Arnaudon, Darryl D. Holm, Akshay Sadananda Uppinakudru Pai, Stefan Horst Sommer

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.

TitelInformation Processing in Medical Imaging : 25th International Conference, IPMI 2017, Boone, NC, USA, June 25-30, 2017, Proceedings
Antal sider12
ISBN (Trykt)978-3-319-59049-3
ISBN (Elektronisk)978-3-319-59050-9
StatusUdgivet - 2017
Begivenhed25th International Conference on Information Processing in Medical Imaging - Boone, USA
Varighed: 25 jun. 201730 jun. 2017
Konferencens nummer: 25


Konference25th International Conference on Information Processing in Medical Imaging
NavnLecture notes in computer science


ID: 184143085