Evaluating Reinforcement Learning Agents for Anatomical Landmark Detection

By A. Alansary, Ozan Oktay, Yuanwei Li, Loic Le Folgoc, Benjamin Hou, G. Vaillant, Konstantinos Kamnitsas, Athanasios Vlontzos, Ben Glocker, Bernhard Kainz and 1 more. 2019

181 citations recorded by the source corpus.

Graphical abstract Figure. No Caption available. 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.

Subfields: Computer Vision and Pattern Recognition

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In Computer Vision and Pattern Recognition

1. Shamim Nemati
2. Chao Yu
3. Jiming Liu
4. Walid Abdullah Al
5. Il Dong Yun

Citations in the corpus

• Reinforcement Learning in Healthcare: A Survey (2019, methodology): “evaluated different kinds of DRL methods (DQN, Double DQN (DDQN), Duel DQN, and Duel DDQN) [12] for anatomical landmark localization in 3D fetal UI [259], and automatic standard view plane detection [260].”
• Partial Policy-Based Reinforcement Learning for Anatomical Landmark Localization in 3D Medical Images (2018, methodology): “In our work, we focus on improving the learning of the navigating agent, keeping the basic formulation similar to the existing works [4]–[6].”