Fast Nonparametric Mutual-Information-based Registration and Uncertainty Estimation

Mikael Agn*, Koen Leemput

*Corresponding author for this work

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

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Abstract

In this paper we propose a probabilistic model for multi-modal non-linear registration that directly incorporates the mutual information (MI) metric into a demons-like optimization scheme. In contrast to uni-modal registration, where the demons algorithm uses repeated spatial filtering to obtain very fast solutions, MI-based registration currently relies on general-purpose optimization schemes that are much slower. The central idea of this work is to reformulate an often-used histogram interpolation technique in MI implementations as an explicit spatial interpolation step within a generative model. By exploiting the specific structure of this model, we obtain a dedicated and fast expectation-maximization optimizer with demons-like properties. This also leads to an easy-to-implement Gibbs sampler to infer registration uncertainty in high-dimensional deformation models, involving very little additional code and no external tuning. Preliminary experiments on multi-modal brain MRI images show that the proposed optimizer can be both faster and more accurate than the free-form deformation method implemented in Elastix. We also demonstrate the sampler’s ability to produce direct uncertainty estimates of MI-based registrations – to the best of our knowledge the first method in the literature to do so.
Original languageEnglish
Title of host publicationUncertainty for Safe Utilization of Machine Learning in Medical Imaging and Clinical Image-Based Procedures. CLIP 2019, UNSURE 2019
PublisherSpringer
Publication date2019
Pages42-51
ISBN (Print)978-3-030-32688-3
ISBN (Electronic)978-3-030-32689-0
DOIs
Publication statusPublished - 2019
Event1st International Workshop on Uncertainty for Safe Utilization of Machine Learning in Medical Imaging - Shenzhen, China
Duration: 17 Oct 201917 Oct 2019
Conference number: 1

Conference

Conference1st International Workshop on Uncertainty for Safe Utilization of Machine Learning in Medical Imaging
Number1
Country/TerritoryChina
CityShenzhen
Period17/10/201917/10/2019
SeriesLecture Notes in Computer Science
Volume11840
ISSN0302-9743

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