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Proceedings Paper

Superpixel-based structure classification for laparoscopic surgery
Author(s): Sebastian Bodenstedt; Jochen Görtler; Martin Wagner; Hannes Kenngott; Beat Peter Müller-Stich; Rüdiger Dillmann; Stefanie Speidel
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Paper Abstract

Minimally-invasive interventions offers multiple benefits for patients, but also entails drawbacks for the surgeon. The goal of context-aware assistance systems is to alleviate some of these difficulties. Localizing and identifying anatomical structures, maligned tissue and surgical instruments through endoscopic image analysis is paramount for an assistance system, making online measurements and augmented reality visualizations possible. Furthermore, such information can be used to assess the progress of an intervention, hereby allowing for a context-aware assistance. In this work, we present an approach for such an analysis. First, a given laparoscopic image is divided into groups of connected pixels, so-called superpixels, using the SEEDS algorithm. The content of a given superpixel is then described using information regarding its color and texture. Using a Random Forest classifier, we determine the class label of each superpixel. We evaluated our approach on a publicly available dataset for laparoscopic instrument detection and achieved a DICE score of 0.69.

Paper Details

Date Published: 18 March 2016
PDF: 6 pages
Proc. SPIE 9786, Medical Imaging 2016: Image-Guided Procedures, Robotic Interventions, and Modeling, 978618 (18 March 2016); doi: 10.1117/12.2216750
Show Author Affiliations
Sebastian Bodenstedt, Karlsruhe Institute of Technology (Germany)
Jochen Görtler, Karlsruhe Institute for Technology (Germany)
Martin Wagner, Univ. of Heidelberg (Germany)
Hannes Kenngott, Univ. of Heidelberg (Germany)
Beat Peter Müller-Stich, Univ. of Heidelberg (Germany)
Rüdiger Dillmann, Karlsruhe Institute of Technology (Germany)
Stefanie Speidel, Karlsruhe Institute of Technology (Germany)

Published in SPIE Proceedings Vol. 9786:
Medical Imaging 2016: Image-Guided Procedures, Robotic Interventions, and Modeling
Robert J. Webster III; Ziv R. Yaniv, Editor(s)

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