Imperial College London
Portrait Picture

Dr Ben Glocker

Faculty of Engineering, Department of Computing

Professor in Machine Learning for Imaging
 
 
 
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Contact

 

+44 (0)20 7594 8334b.glocker Website CV

 
 
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Location

 

377Huxley BuildingSouth Kensington Campus

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Summary

 

Publications

Citation

BibTex format

@article{Hou:2018:10.1109/TMI.2018.2798801,
author = {Hou, B and Khanal, B and Alansary, A and McDonagh, S and Davidson, A and Rutherford, M and Hajnal, J and Rueckert, D and Glocker, B and Kainz, B},
doi = {10.1109/TMI.2018.2798801},
journal = {IEEE Transactions on Medical Imaging},
pages = {1737--1750},
title = {3D reconstruction in canonical co-ordinate space from arbitrarily oriented 2D images},
url = {http://dx.doi.org/10.1109/TMI.2018.2798801},
volume = {37},
year = {2018}
}
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RIS format (EndNote, RefMan)

TY  - JOUR
AB  - Limited capture range, and the requirement to provide high quality initialization for optimization-based 2D/3D image registration methods, can significantly degrade the performance of 3D image reconstruction and motion compensation pipelines. Challenging clinical imaging scenarios, which contain significant subject motion such as fetal in-utero imaging, complicate the 3D image and volume reconstruction process. In this paper we present a learning based image registration method capable of predicting 3D rigid transformations of arbitrarily oriented 2D image slices, with respect to a learned canonical atlas co-ordinate system. Only image slice intensity information is used to perform registration and canonical alignment, no spatial transform initialization is required. To find image transformations we utilize a Convolutional Neural Network (CNN) architecture to learn the regression function capable of mapping 2D image slices to a 3D canonical atlas space. We extensively evaluate the effectiveness of our approach quantitatively on simulated Magnetic Resonance Imaging (MRI), fetal brain imagery with synthetic motion and further demonstrate qualitative results on real fetal MRI data where our method is integrated into a full reconstruction and motion compensation pipeline. Our learning based registration achieves an average spatial prediction error of 7 mm on simulated data and produces qualitatively improved reconstructions for heavily moving fetuses with gestational ages of approximately 20 weeks. Our model provides a general and computationally efficient solution to the 2D/3D registration initialization problem and is suitable for real-time scenarios.
AU  - Hou,B
AU  - Khanal,B
AU  - Alansary,A
AU  - McDonagh,S
AU  - Davidson,A
AU  - Rutherford,M
AU  - Hajnal,J
AU  - Rueckert,D
AU  - Glocker,B
AU  - Kainz,B
DO  - 10.1109/TMI.2018.2798801
EP  - 1750
PY  - 2018///
SN  - 0278-0062
SP  - 1737
TI  - 3D reconstruction in canonical co-ordinate space from arbitrarily oriented 2D images
T2  - IEEE Transactions on Medical Imaging
UR  - http://dx.doi.org/10.1109/TMI.2018.2798801
UR  - http://hdl.handle.net/10044/1/56337
VL  - 37
ER  -
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