Imperial College London
Portrait Picture

DR BERNHARD KAINZ

Faculty of Engineering, Department of Computing

Reader in Medical Image Computing
 
 
 
//

Contact

 

+44 (0)20 7594 8349b.kainz Website CV

 
 
//

Location

 

372Huxley BuildingSouth Kensington Campus

//

Summary

 

Publications

Citation

BibTex format

@inproceedings{Hou:2020:10.1007/978-3-030-33843-5_13,
author = {Hou, B and Vlontzos, A and Alansary, A and Rueckert, D and Kainz, B},
doi = {10.1007/978-3-030-33843-5_13},
pages = {139--150},
publisher = {Springer International Publishing},
title = {Flexible conditional image generation of missing data with learned mental maps},
url = {http://dx.doi.org/10.1007/978-3-030-33843-5_13},
year = {2020}
}
Download

RIS format (EndNote, RefMan)

TY  - CPAPER
AB  - Real-world settings often do not allow acquisition of high-resolution volumetric images for accurate morphological assessment and diagnostic. In clinical practice it is frequently common to acquire only sparse data (e.g. individual slices) for initial diagnostic decision making. Thereby, physicians rely on their prior knowledge (or mental maps) of the human anatomy to extrapolate the underlying 3D information. Accurate mental maps require years of anatomy training, which in the first instance relies on normative learning, i.e. excluding pathology. In this paper, we leverage Bayesian Deep Learning and environment mapping to generate full volumetric anatomy representations from none to a small, sparse set of slices. We evaluate proof of concept implementations based on Generative Query Networks (GQN) and Conditional BRUNO using abdominal CT and brain MRI as well as in a clinical application involving sparse, motion-corrupted MR acquisition for fetal imaging. Our approach allows to reconstruct 3D volumes from 1 to 4 tomographic slices, with a SSIM of 0.7+ and cross-correlation of 0.8+ compared to the 3D ground truth.
AU  - Hou,B
AU  - Vlontzos,A
AU  - Alansary,A
AU  - Rueckert,D
AU  - Kainz,B
DO  - 10.1007/978-3-030-33843-5_13
EP  - 150
PB  - Springer International Publishing
PY  - 2020///
SN  - 0302-9743
SP  - 139
TI  - Flexible conditional image generation of missing data with learned mental maps
UR  - http://dx.doi.org/10.1007/978-3-030-33843-5_13
UR  - http://hdl.handle.net/10044/1/83337
ER  -
Download