A stochastic large deformation model for computational anatomy

Research output: Chapter in Book/Report/Conference proceedingArticle in proceedingsResearchpeer-review

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.

Original languageEnglish
Title of host publicationInformation Processing in Medical Imaging : 25th International Conference, IPMI 2017, Boone, NC, USA, June 25-30, 2017, Proceedings
Number of pages12
PublisherSpringer
Publication date2017
Pages571-582
ISBN (Print)978-3-319-59049-3
ISBN (Electronic)978-3-319-59050-9
DOIs
Publication statusPublished - 2017
Event25th International Conference on Information Processing in Medical Imaging - Boone, United States
Duration: 25 Jun 201730 Jun 2017
Conference number: 25

Conference

Conference25th International Conference on Information Processing in Medical Imaging
Nummer25
LandUnited States
ByBoone
Periode25/06/201730/06/2017
SeriesLecture notes in computer science
Volume10265
ISSN0302-9743

    Research areas

  • Computational anatomy, Large deformations, LDDMM, Stochastic processes

Links

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