Multiple landmark detection using multi-agent reinforcement learning
File(s)
Accepted version
OA Location
Author(s)
Vlontzos, Athanasios
Alansary, Amir
Kamnitsas, Konstantinos
Rueckert, Daniel
Kainz, Bernhard
Type
Conference Paper
Abstract
The detection of anatomical landmarks is a vital step for medical image analysis and applications for diagnosis, interpretation and guidance. Manual annotation of landmarks is a tedious process that requires domain-specific expertise and introduces inter-observer variability. This paper proposes a new detection approach for multiple landmarks based on multi-agent reinforcement learning. Our hypothesis is that the position of all anatomical landmarks is interdependent and non-random within the human anatomy, thus finding one landmark can help to deduce the location of others. Using a Deep Q-Network (DQN) architecture we construct an environment and agent with implicit inter-communication such that we can accommodate K agents acting and learning simultaneously, while they attempt to detect K different landmarks. During training the agents collaborate by sharing their accumulated knowledge for a collective gain. We compare our approach with state-of-the-art architectures and achieve significantly better accuracy by reducing the detection error by 50%, while requiring fewer computational resources and time to train compared to the naïve approach of training K agents separately. Code and visualizations available: https://github.com/thanosvlo/MARL-for-Anatomical-Landmark-Detection
Editor(s)
Shen, D
Liu, T
Peters, TM
Staib, LH
Essert, C
Zhou, S
Yap, PT
Khan, A
Date Issued
2019-10-13
Date Acceptance
2019-06-29
Citation
MICCAI 2019: Medical Image Computing and Computer Assisted Intervention, 2019, 11767, pp.262-270
ISBN
978-3-030-32250-2
ISSN
0302-9743
Publisher
Springer International Publishing AG
Start Page
262
End Page
270
Journal / Book Title
MICCAI 2019: Medical Image Computing and Computer Assisted Intervention
Volume
11767
Copyright Statement
© Springer Nature Switzerland AG 2019. The final publication is available at Springer via https://link.springer.com/chapter/10.1007%2F978-3-030-32251-9_29
Sponsor
Engineering and Physical Sciences Research Council (EPSRC)
Engineering & Physical Science Research Council (EPSRC)
Identifier
http://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcApp=PARTNER_APP&SrcAuth=LinksAMR&KeyUT=WOS:000548735900029&DestLinkType=FullRecord&DestApp=ALL_WOS&UsrCustomerID=1ba7043ffcc86c417c072aa74d649202
Grant Number
EP/S013687/1
EP/S013687/1
Source
10th International Workshop on Machine Learning in Medical Imaging (MLMI) / 22nd International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI)
Subjects
Science & Technology
Technology
Life Sciences & Biomedicine
Computer Science, Artificial Intelligence
Computer Science, Software Engineering
Engineering, Biomedical
Neuroimaging
Imaging Science & Photographic Technology
Radiology, Nuclear Medicine & Medical Imaging
Computer Science
Engineering
Neurosciences & Neurology
Publication Status
Published
Start Date
2019-10-13
Finish Date
2019-10-17
Coverage Spatial
Shenzhen, China
Date Publish Online
2019-10-10