Automatic classification of minimally invasive instruments based on endoscopic image sequences

Stefanie Speidel; Julia Benzko; Sebastian Krappe; Gunther Sudra; Pedram Azad; Beat Peter Müller-Stich; Carsten Gutt; Rüdiger Dillmann

doi:10.1117/12.811112

13 March 2009 Automatic classification of minimally invasive instruments based on endoscopic image sequences

Stefanie Speidel, Julia Benzko, Sebastian Krappe, Gunther Sudra, Pedram Azad, Beat Peter Müller-Stich, Carsten Gutt, Rüdiger Dillmann

Author Affiliations +

Proceedings Volume 7261, Medical Imaging 2009: Visualization, Image-Guided Procedures, and Modeling; 72610A (2009) https://doi.org/10.1117/12.811112
Event: SPIE Medical Imaging, 2009, Lake Buena Vista (Orlando Area), Florida, United States

Abstract

Minimally invasive surgery is nowadays a frequently applied technique and can be regarded as a major breakthrough in surgery. The surgeon has to adopt special operation-techniques and deal with difficulties like the complex hand-eye coordination and restricted mobility. To alleviate these constraints we propose to enhance the surgeon's capabilities by providing a context-aware assistance using augmented reality techniques. To analyze the current situation for context-aware assistance, we need intraoperatively gained sensor data and a model of the intervention. A situation consists of information about the performed activity, the used instruments, the surgical objects, the anatomical structures and defines the state of an intervention for a given moment in time. The endoscopic images provide a rich source of information which can be used for an image-based analysis. Different visual cues are observed in order to perform an image-based analysis with the objective to gain as much information as possible about the current situation. An important visual cue is the automatic recognition of the instruments which appear in the scene. In this paper we present the classification of minimally invasive instruments using the endoscopic images. The instruments are not modified by markers. The system segments the instruments in the current image and recognizes the instrument type based on three-dimensional instrument models.

Citation Download Citation

Stefanie Speidel, Julia Benzko, Sebastian Krappe, Gunther Sudra, Pedram Azad, Beat Peter Müller-Stich, Carsten Gutt, and Rüdiger Dillmann "Automatic classification of minimally invasive instruments based on endoscopic image sequences", Proc. SPIE 7261, Medical Imaging 2009: Visualization, Image-Guided Procedures, and Modeling, 72610A (13 March 2009); https://doi.org/10.1117/12.811112

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available