Imperial College London
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DR BERNHARD KAINZ

Faculty of Engineering, Department of Computing

Reader in Medical Image Computing
 
 
 
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Contact

 

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

 
 
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Location

 

372Huxley BuildingSouth Kensington Campus

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Summary

 

Publications

Citation

BibTex format

@article{Baumgartner:2017:10.1109/TMI.2017.2712367,
author = {Baumgartner, C and Kamnitsas, K and Matthew, J and Fletcher, TP and Smith, S and Koch, L and Kainz, B and Rueckert, D},
doi = {10.1109/TMI.2017.2712367},
journal = {IEEE Transactions on Medical Imaging},
pages = {2204--2215},
title = {SonoNet: real-time detection and localisation of fetal standard scan planes in freehand ultrasound},
url = {http://dx.doi.org/10.1109/TMI.2017.2712367},
volume = {36},
year = {2017}
}
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RIS format (EndNote, RefMan)

TY  - JOUR
AB  - Identifying and interpreting fetal standard scan planes during 2D ultrasound mid-pregnancy examinations are highly complex tasks which require years of training. Apart from guiding the probe to the correct location, it can be equally difficult for a non-expert to identify relevant structures within the image. Automatic image processing can provide tools to help experienced as well as inexperienced operators with these tasks. In this paper, we propose a novel method based on convolutional neural networks which can automatically detect 13 fetal standard views in freehand 2D ultrasound data as well as provide a localisation of the fetal structures via a bounding box. An important contribution is that the network learns to localise the target anatomy using weak supervision based on image-level labels only. The network architecture is designed to operate in real-time while providing optimal output for the localisation task. We present results for real-time annotation, retrospective frame retrieval from saved videos, and localisation on a very large and challenging dataset consisting of images and video recordings of full clinical anomaly screenings. We found that the proposed method achieved an average F1-score of 0.798 in a realistic classification experiment modelling real-time detection, and obtained a 90.09% accuracy for retrospective frame retrieval. Moreover, an accuracy of 77.8% was achieved on the localisation task.
AU  - Baumgartner,C
AU  - Kamnitsas,K
AU  - Matthew,J
AU  - Fletcher,TP
AU  - Smith,S
AU  - Koch,L
AU  - Kainz,B
AU  - Rueckert,D
DO  - 10.1109/TMI.2017.2712367
EP  - 2215
PY  - 2017///
SN  - 1558-254X
SP  - 2204
TI  - SonoNet: real-time detection and localisation of fetal standard scan planes in freehand ultrasound
T2  - IEEE Transactions on Medical Imaging
UR  - http://dx.doi.org/10.1109/TMI.2017.2712367
UR  - http://hdl.handle.net/10044/1/48886
VL  - 36
ER  -
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