Ultrasound-Guided Robotic Navigation with Deep Reinforcement Learning
Hannes Hase, Mohammad Farid Azampour, Maria Tirindelli, Magdalini Paschali, Walter Simson, Emad Fatemizadeh, Nassir Navab
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.
BibTeX
@inproceedings{iros2020_ultrasoundguided,
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},
booktitle = {IROS 2020},
year = {2020}
}