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

@inproceedings{Liu:2020:10.1007/978-3-030-59716-0_46,
author = {Liu, T and Meng, Q and Vlontzos, A and Tan, J and Rueckert, D and Kainz, B},
doi = {10.1007/978-3-030-59716-0_46},
pages = {483--492},
publisher = {Springer International Publishing},
title = {Ultrasound video summarization using deep reinforcement learning},
url = {http://dx.doi.org/10.1007/978-3-030-59716-0_46},
year = {2020}
}
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RIS format (EndNote, RefMan)

TY  - CPAPER
AB  - Video is an essential imaging modality for diagnostics, e.g. in ultrasound imaging, for endoscopy, or movement assessment. However, video hasn’t received a lot of attention in the medical image analysis community. In the clinical practice, it is challenging to utilise raw diagnostic video data efficiently as video data takes a long time to process, annotate or audit. In this paper we introduce a novel, fully automatic video summarization method that is tailored to the needs of medical video data. Our approach is framed as reinforcement learning problem and produces agents focusing on the preservation of important diagnostic information. We evaluate our method on videos from fetal ultrasound screening, where commonly only a small amount of the recorded data is used diagnostically. We show that our method is superior to alternative video summarization methods and that it preserves essential information required by clinical diagnostic standards.
AU  - Liu,T
AU  - Meng,Q
AU  - Vlontzos,A
AU  - Tan,J
AU  - Rueckert,D
AU  - Kainz,B
DO  - 10.1007/978-3-030-59716-0_46
EP  - 492
PB  - Springer International Publishing
PY  - 2020///
SN  - 0302-9743
SP  - 483
TI  - Ultrasound video summarization using deep reinforcement learning
UR  - http://dx.doi.org/10.1007/978-3-030-59716-0_46
UR  - https://link.springer.com/chapter/10.1007%2F978-3-030-59716-0_46
UR  - http://hdl.handle.net/10044/1/87508
ER  -
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