PuSH - Publication Server of Helmholtz Zentrum München

Sideri-Lampretsa, V.* ; Zimmer, V.A.* ; Qiu, H.* ; Kaissis, G. ; Rueckert, D.*

MAD: Modality Agnostic Distance Measure for Image Registration.

In:. Berlin [u.a.]: Springer, 2023. 147-156 (Lect. Notes Comput. Sc. ; 14394 LNCS)
DOI
Open Access Green as soon as Postprint is submitted to ZB.
Multi-modal image registration is a crucial pre-processing step in many medical applications. However, it is a challenging task due to the complex intensity relationships between different imaging modalities, which can result in large discrepancy in image appearance. The success of multi-modal image registration, whether it is conventional or learning based, is predicated upon the choice of an appropriate distance (or similarity) measure. Particularly, deep learning registration algorithms lack in accuracy or even fail completely when attempting to register data from an “unseen” modality. In this work, we present Modality Agnostic Distance (MAD), a deep image distance measure that utilises random convolutions to learn the inherent geometry of the images while being robust to large appearance changes. Random convolutions are geometry-preserving modules which we use to simulate an infinite number of synthetic modalities alleviating the need for aligned paired data during training. We can therefore train MAD on a mono-modal dataset and successfully apply it to a multi-modal dataset. We demonstrate that not only can MAD affinely register multi-modal images successfully, but it has also a larger capture range than traditional measures such as Mutual Information and Normalised Gradient Fields. Our code is available at: https://github.com/ModalityAgnosticDistance/MAD.
Altmetric
Additional Metrics?
[➜Log in]
Edit extra informations Login
Publication type Article: Conference contribution
Corresponding Author
Keywords Distance Measure ; Image Registration ; Mutli-modality; Entropy
ISSN (print) / ISBN 0302-9743
e-ISSN 1611-3349
Journal Lecture Notes in Computer Science
Quellenangaben Volume: 14394 LNCS, Issue: , Pages: 147-156 Article Number: , Supplement: ,
Publisher Springer
Publishing Place Berlin [u.a.]
Non-patent literature Publications
Institute(s) Helmholtz AI - HMGU (HAI - HMGU)
Institute for Machine Learning in Biomed Imaging (IML)