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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 möglich sobald Postprint bei der ZB eingereicht worden ist.
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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Publikationstyp Artikel: Konferenzbeitrag
Korrespondenzautor
ISSN (print) / ISBN 0302-9743
e-ISSN 1611-3349
Konferenztitel International Workshop on Preterm, Perinatal and Paediatric Image Analysis
Zeitschrift Lecture Notes in Computer Science
Quellenangaben Band: 11076 LNCS, Heft: , Seiten: 149-159 Artikelnummer: , Supplement: ,
Verlag Springer
Verlagsort Berlin [u.a.]
Nichtpatentliteratur Publikationen
Institut(e) Institute for Machine Learning in Biomed Imaging (IML)