Landmark Detection in 3D Medical Images Using Reinforcement Learning

Kundeti, SR and Parmar, D and Mv, AS and Gautam, D (2020) Landmark Detection in 3D Medical Images Using Reinforcement Learning. In: 9th IEEE International Conference on Cloud Computing in Emerging Markets, CCEM 2020, 4-7 Nov 2020, pp. 42-46.

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Abstract

Computer vision tasks like key point localization for face recognition and pose estimation uses Deep Learning (DL) and Reinforcement Learning (RL). Similarly for medical image analysis, anatomical landmark detection is beneficial for automatic scan planning which reduces technician's time and error. This paper compares DL and RL models and variants of Q-learning methods like Deep Q-Network in a single and multi-agent setting. Results show that RL approaches are performing significantly better as compared to DL approaches. All the metrics are in mm. For five landmarks, with single-agent RL, Mean absolute error and Mean euclidean error are 1.66 mm and 3.87 mm, whereas, with multi-agent RL, errors are 2.12 mm and 4.33 mm respectively on the test set (n=60). While with DL approach, errors are 6.54 mm and 14.62 mm which is nearly 3 to 4 times more than RL based approach. The performance of all variants of the DQN approach is excellent, where Double DQN is performing best with errors as 2.10 mm and 4.34 mm. © 2020 IEEE.

Item Type:	Conference Paper
Publication:	Proceedings - 2020 IEEE International Conference on Cloud Computing in Emerging Markets, CCEM 2020
Publisher:	Institute of Electrical and Electronics Engineers Inc.
Additional Information:	The copyright for this article belongs to Institute of Electrical and Electronics Engineers Inc.
Keywords:	Cloud computing; Commerce; Deep learning; Errors; Face recognition; Learning systems; Medical imaging; Multi agent systems, 3D medical image; Anatomical landmarks; Landmark detection; Mean absolute error; Multi-agent setting; Pose estimation; Q-learning method; Single-agent, Reinforcement learning
Department/Centre:	Division of Electrical Sciences > Electrical Communication Engineering
Date Deposited:	03 Dec 2021 08:31
Last Modified:	03 Dec 2021 08:31
URI:	http://eprints.iisc.ac.in/id/eprint/70218

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