Learning Dynamic Spatial Relations - The Case of a Knowledge-based Endoscopic Camera Guidance Robot

Andreas Bihlmaier describes a novel method to model dynamic spatial relations by machine learning techniques. The method is applied to the task of representing the tacit knowledge of a trained camera assistant in minimally-invasive surgery. The model is then used for intraoperative control of a robot that autonomously positions the endoscope. Furthermore, a modular robotics platform is described, which forms the basis for this knowledge-based assistance system. Promising results from a complex phantom study are presented.

Inhaltsverzeichnis

Endoscope Robots and Automated Camera Guidance.- Knowledge-based Cognitive Systems.- Modular Research Platform for Robot-Assisted Surgery based on ROS.- Learning of Surgical Know-how by Models of Spatial Relations.- Intraoperative Camera Assistance.- Evaluation Study: TME in the Open Source Heidelberg Laparoscopic Phantom (OpenHELP).

Über den Autor / die Autorin

Andreas Bihlmaier is leader of the Cognitive Medical Technologies group in the Institute for Anthropomatics and Robotics – Intelligent Process Control and Robotics Lab (IAR-IPR) at the Karlsruhe Institute of Technology (KIT). His research focuses on cognitive surgical robotics for minimally-invasive surgery, as part of the SFB/Transregio 125 “Cognition-Guided Surgery”.

Produktdetails

• Autoren: Andreas Bihlmaier
• Verlag: Springer, Berlin
• Sprache: Englisch
• Produktform: Taschenbuch
• Erschienen: 18.08.2016
• EAN: 9783658149130
• ISBN: 978-3-658-14913-0
• Seiten: 267
• Abmessung: 184 mm x 15 mm x 211 mm
• Gewicht: 371 g
• Illustration: XV, 267 p. 120 illus.
• Themen: Naturwissenschaften, Medizin, Informatik, Technik > Informatik, EDV > Informatik; C, Artificial Intelligence, Mustererkennung, Chirurgische Techniken, computer science, pattern recognition, surgical techniques, Automated Pattern Recognition, minimally invasive surgery, Pattern recognition systems