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2018 | OriginalPaper | Chapter

LSTM Spatial Co-transformer Networks for Registration of 3D Fetal US and MR Brain Images

Authors : Robert Wright, Bishesh Khanal, Alberto Gomez, Emily Skelton, Jacqueline Matthew, Jo V. Hajnal, Daniel Rueckert, Julia A. Schnabel

Published in: Data Driven Treatment Response Assessment and Preterm, Perinatal, and Paediatric Image Analysis

Publisher: Springer International Publishing

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Abstract

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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Title: LSTM Spatial Co-transformer Networks for Registration of 3D Fetal US and MR Brain Images
Authors: Robert Wright
Bishesh Khanal
Alberto Gomez
Emily Skelton
Jacqueline Matthew
Jo V. Hajnal
Daniel Rueckert
Julia A. Schnabel
Publisher: Springer International Publishing
Book: Data Driven Treatment Response Assessment and Preterm, Perinatal, and Paediatric Image Analysis
Print ISBN: 978-3-030-00806-2

Electronic ISBN: 978-3-030-00807-9

Copyright Year: 2018
DOI: https://doi.org/10.1007/978-3-030-00807-9_15

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