Imperial College London

ProfessorDanielRueckert

Faculty of EngineeringDepartment of Computing

Head of Department of Computing
 
 
 
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Contact

 

+44 (0)20 7594 8333d.rueckert Website

 
 
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Location

 

568Huxley BuildingSouth Kensington Campus

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Summary

 

Publications

Citation

BibTex format

@inproceedings{Cerrolaza:2018:10.1007/978-3-030-00928-1_44,
author = {Cerrolaza, JJ and Li, Y and Biffi, C and Gomez, A and Sinclair, M and Matthew, J and Knight, C and Kainz, B and Rueckert, D},
doi = {10.1007/978-3-030-00928-1_44},
pages = {383--391},
title = {3D fetal skull reconstruction from 2DUS via deep conditional generative networks},
url = {http://dx.doi.org/10.1007/978-3-030-00928-1_44},
year = {2018}
}

RIS format (EndNote, RefMan)

TY  - CPAPER
AB - © Springer Nature Switzerland AG 2018. 2D ultrasound (US) is the primary imaging modality in antenatal healthcare. Despite the limitations of traditional 2D biometrics to characterize the true 3D anatomy of the fetus, the adoption of 3DUS is still very limited. This is particularly significant in developing countries and remote areas, due to the lack of experienced sonographers and the limited access to 3D technology. In this paper, we present a new deep conditional generative network for the 3D reconstruction of the fetal skull from 2DUS standard planes of the head routinely acquired during the fetal screening process. Based on the generative properties of conditional variational autoencoders (CVAE), our reconstruction architecture (REC-CVAE) directly integrates the three US standard planes as conditional variables to generate a unified latent space of the skull. Additionally, we propose HiREC-CVAE, a hierarchical generative network based on the different clinical relevance of each predictive view. The hierarchical structure of HiREC-CVAE allows the network to learn a sequence of nested latent spaces, providing superior predictive capabilities even in the absence of some of the 2DUS scans. The performance of the proposed architectures was evaluated on a dataset of 72 cases, showing accurate reconstruction capabilities from standard non-registered 2DUS images.
AU - Cerrolaza,JJ
AU - Li,Y
AU - Biffi,C
AU - Gomez,A
AU - Sinclair,M
AU - Matthew,J
AU - Knight,C
AU - Kainz,B
AU - Rueckert,D
DO - 10.1007/978-3-030-00928-1_44
EP - 391
PY - 2018///
SN - 0302-9743
SP - 383
TI - 3D fetal skull reconstruction from 2DUS via deep conditional generative networks
UR - http://dx.doi.org/10.1007/978-3-030-00928-1_44
ER -