Imperial College London

ProfessorDanielRueckert

Faculty of EngineeringDepartment of Computing

Head of Department of Computing
 
 
 
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Contact

 

+44 (0)20 7594 8333d.rueckert Website

 
 
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Location

 

568Huxley BuildingSouth Kensington Campus

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Summary

 

Publications

Citation

BibTex format

@article{Alansary:2019:10.1016/j.media.2019.02.007,
author = {Alansary, A and Oktay, O and Li, Y and Folgoc, LL and Hou, B and Vaillant, G and Kamnitsas, K and Vlontzos, A and Glocker, B and Kainz, B and Rueckert, D},
doi = {10.1016/j.media.2019.02.007},
journal = {Medical Image Analysis},
pages = {156--164},
title = {Evaluating reinforcement learning agents for anatomical landmark detection},
url = {http://dx.doi.org/10.1016/j.media.2019.02.007},
volume = {53},
year = {2019}
}

RIS format (EndNote, RefMan)

TY  - JOUR
AB - Automatic detection of anatomical landmarks is an important step for a wide range of applications in medical image analysis. Manual annotation of landmarks is a tedious task and prone to observer errors. In this paper, we evaluate novel deep reinforcement learning (RL) strategies to train agents that can precisely and robustly localize target landmarks in medical scans. An artificial RL agent learns to identify the optimal path to the landmark by interacting with an environment, in our case 3D images. Furthermore, we investigate the use of fixed- and multi-scale search strategies with novel hierarchical action steps in a coarse-to-fine manner. Several deep Q-network (DQN) architectures are evaluated for detecting multiple landmarks using three different medical imaging datasets: fetal head ultrasound (US), adult brain and cardiac magnetic resonance imaging (MRI). The performance of our agents surpasses state-of-the-art supervised and RL methods. Our experiments also show that multi-scale search strategies perform significantly better than fixed-scale agents in images with large field of view and noisy background such as in cardiac MRI. Moreover, the novel hierarchical steps can significantly speed up the searching process by a factor of 4-5 times.
AU - Alansary,A
AU - Oktay,O
AU - Li,Y
AU - Folgoc,LL
AU - Hou,B
AU - Vaillant,G
AU - Kamnitsas,K
AU - Vlontzos,A
AU - Glocker,B
AU - Kainz,B
AU - Rueckert,D
DO - 10.1016/j.media.2019.02.007
EP - 164
PY - 2019///
SN - 1361-8415
SP - 156
TI - Evaluating reinforcement learning agents for anatomical landmark detection
T2 - Medical Image Analysis
UR - http://dx.doi.org/10.1016/j.media.2019.02.007
UR - https://www.ncbi.nlm.nih.gov/pubmed/30784956
UR - http://hdl.handle.net/10044/1/67534
VL - 53
ER -