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Evaluating reinforcement learning agents for anatomical landmark detection.

Authors :
Alansary, Amir
Oktay, Ozan
Li, Yuanwei
Folgoc, Loic Le
Hou, Benjamin
Vaillant, Ghislain
Kamnitsas, Konstantinos
Vlontzos, Athanasios
Glocker, Ben
Kainz, Bernhard
Rueckert, Daniel
Source :
Medical Image Analysis. Apr2019, Vol. 53, p156-164. 9p.
Publication Year :
2019

Abstract

Graphical abstract Abstract 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. [ABSTRACT FROM AUTHOR]

Details

Language :
English
ISSN :
13618415
Volume :
53
Database :
Academic Search Index
Journal :
Medical Image Analysis
Publication Type :
Academic Journal
Accession number :
134958425
Full Text :
https://doi.org/10.1016/j.media.2019.02.007