VesNet-RL: Simulation-Based Reinforcement Learning for Real-World US Probe Navigation

Bi Y, Jiang Z, Gao Y, Wendler T, Karlas A, Navab N (2022)

Publication Type: Journal article

Publication year: 2022

Journal

IEEE Robotics and Automation Letters IEEE

Book Volume: 7

Pages Range: 6638-6645

Journal Issue: 3

DOI: 10.1109/LRA.2022.3176112

Abstract

Ultrasound (US) is one of the most common medical imaging modalities since it is radiation-free, low-cost, and real-time. In freehand US examinations, sonographers often navigate a US probe to visualize standard examination planes with rich diagnostic information. However, reproducibility and stability of the resulting images often suffer from intra- and inter-operator variation. Reinforcement learning (RL), as an interaction-based learning method, has demonstrated its effectiveness in visual navigating tasks; however, RL is limited in terms of generalization. To address this challenge, we propose a simulation-based RL framework for real-world navigation of US probes towards the standard longitudinal views of vessels. A UNet is used to provide binary masks from US images; thereby, the RL agent trained on simulated binary vessel images can be applied in real scenarios without further training. To accurately characterize actual states, a multi-modality state representation structure is introduced to facilitate the understanding of environments. Moreover, considering the characteristics of vessels, a novel standard view recognition approach based on the minimum bounding rectangle is proposed to terminate the searching process. To evaluate the effectiveness of the proposed method, the trained policy is validated virtually on 3D volumes of a volunteer's in-vivo carotid artery, and physically on custom-designed gel phantoms using robotic US. The results demonstrate that proposed approach can effectively and accurately navigate the probe towards the longitudinal view of vessels.

Involved external institutions

Ludwig-Maximilians-Universität (LMU) DE Germany (DE) Technische Universität München (TUM) DE Germany (DE) Helmholtz Zentrum München - Deutsches Forschungszentrum für Gesundheit und Umwelt (HMGU) / Helmholtz Munich DE Germany (DE)

How to cite

APA:

Bi, Y., Jiang, Z., Gao, Y., Wendler, T., Karlas, A., & Navab, N. (2022). VesNet-RL: Simulation-Based Reinforcement Learning for Real-World US Probe Navigation. IEEE Robotics and Automation Letters, 7(3), 6638-6645. https://doi.org/10.1109/LRA.2022.3176112

MLA:

Bi, Yuan, et al. "VesNet-RL: Simulation-Based Reinforcement Learning for Real-World US Probe Navigation." IEEE Robotics and Automation Letters 7.3 (2022): 6638-6645.

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