Ultrasound video summarization using deep reinforcement learning
File(s)
Accepted version
Author(s)
Type
Conference Paper
Abstract
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.
Date Issued
2020-10-01
Date Acceptance
2020-06-23
Citation
2020, 12263, pp.483-492
ISBN
9783030597153
ISSN
0302-9743
Publisher
Springer International Publishing
Start Page
483
End Page
492
Volume
12263
Copyright Statement
© Springer Nature Switzerland AG 2020. The final publication is available at Springer via https://doi.org/10.1007/978-3-030-59716-0_46
Sponsor
Engineering & Physical Science Research Council (E
Wellcome Trust
Identifier
https://link.springer.com/chapter/10.1007%2F978-3-030-59716-0_46
Grant Number
RTJ5557761-1
PO :RTJ5557761-1
Source
23rd INTERNATIONAL CONFERENCE ON MEDICAL IMAGE COMPUTING & COMPUTER ASSISTED INTERVENTION
Subjects
Artificial Intelligence & Image Processing
Publication Status
Published
Start Date
2020-10-04
Finish Date
2020-10-08
Coverage Spatial
Lima, Peru
Date Publish Online
2020-09-29