Ultrasound-Guided Robotic Navigation with Deep Reinforcement Learning
| Autor: | Emad Fatemizadeh, Magdalini Paschali, Nassir Navab, Mohammad Farid Azampour, Maria Tirindelli, Hannes Hase, Walter Simson |
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| Rok vydání: | 2020 |
| Předmět: |
FOS: Computer and information sciences
Computer Science - Machine Learning Robotic navigation Computer science business.industry Supervised learning Machine Learning (stat.ML) Ultrasound guided Machine Learning (cs.LG) 030218 nuclear medicine & medical imaging Computer Science - Robotics 03 medical and health sciences 0302 clinical medicine Binary classification Statistics - Machine Learning Task analysis Reinforcement learning 030211 gastroenterology & hepatology Computer vision Artificial intelligence business Robotics (cs.RO) |
| Zdroj: | IROS |
| DOI: | 10.1109/iros45743.2020.9340913 |
| Popis: | 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. Comment: Submitted for IROS 2020 |
| Databáze: | OpenAIRE |
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