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Wright, R.* ; Khanal, B.* ; Gomez, A.* ; Skelton, E.* ; Matthew, J.* ; Hajnal, J.V.* ; Rueckert, D.* ; Schnabel, J.A.*

LSTM spatial co-transformer networks for registration of 3D fetal US and MR brain images.

In: (International Workshop on Preterm, Perinatal and Paediatric Image Analysis). Berlin [u.a.]: Springer, 2018. 149-159 (Lect. Notes Comput. Sc. ; 11076 LNCS)
DOI
Open Access Green as soon as Postprint is submitted to ZB.
In this work, we propose a deep learning-based method for iterative registration of fetal brain images acquired by ultrasound and magnetic resonance, inspired by “Spatial Transformer Networks”. Images are co-aligned to a dual modality spatio-temporal atlas, where computational image analysis may be performed in the future. Our results show better alignment accuracy compared to “Self-Similarity Context descriptors”, a state-of-the-art method developed for multi-modal image registration. Furthermore, our method is robust and able to register highly misaligned images, with any initial orientation, where similarity-based methods typically fail.
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Publication type Article: Conference contribution
Corresponding Author
ISSN (print) / ISBN 0302-9743
e-ISSN 1611-3349
Conference Title International Workshop on Preterm, Perinatal and Paediatric Image Analysis
Journal Lecture Notes in Computer Science
Quellenangaben Volume: 11076 LNCS, Issue: , Pages: 149-159 Article Number: , Supplement: ,
Publisher Springer
Publishing Place Berlin [u.a.]
Non-patent literature Publications
Institute(s) Institute for Machine Learning in Biomed Imaging (IML)