Ultrasound-Guided Robotic Navigation with Deep Reinforcement Learning

Hannes Hase, Mohammad Farid Azampour, Maria Tirindelli, Magdalini Paschali, Walter Simson +2 more
3/30/2020
cs.LGcs.ROstat.ML

Official code: hhase/spinal-navigation-rl (29 stars, MIT, last commit 2022-11-22). 1 more implementation below.

Abstract

In this paper we introduce the first reinforcement learning (RL) based robotic navigation method which utilizes ultrasound (US) images as an input. Our approach combines state-of-the-art RL techniques, specifically deep Q-networks (DQN) with memory buffers and a binary classifier for deciding when to terminate the task. Our method is trained and evaluated on an in-house collected data-set of 34 volunteers and when compared to pure RL and supervised learning (SL) techniques, it performs substantially better, which highlights the suitability of RL navigation for US-guided procedures. When testing our proposed model, we obtained a 82.91% chance of navigating correctly to the sacrum from 165 different starting positions on 5 different unseen simulated environments.

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Code Implementations(2)

hhase/spinal-navigation-rlOfficial100%
MIT

Code for: "Ultrasound-Guided Robotic Navigation with Deep Reinforcement Learning"

292PythonMar 1, 20203 years agoMIT
hhase/sacrum_datasetOfficial100%
MIT
40PythonMar 10, 20206 years agoMIT

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Cite this paper

@article{hase2020ultrasoundguided,
  title  = {Ultrasound-Guided Robotic Navigation with Deep Reinforcement Learning},
  author = {Hannes Hase and Mohammad Farid Azampour and Maria Tirindelli and Magdalini Paschali and Walter Simson and Emad Fatemizadeh and Nassir Navab},
  year   = {2020},
  eprint = {2003.13321},
  archivePrefix = {arXiv},
  url    = {https://arxiv.org/abs/2003.13321}
}

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