Superpixel-based structure classification for laparoscopic surgery

Sebastian Bodenstedt; Jochen Görtler; Martin Wagner; Hannes Kenngott; Beat Peter Müller-Stich; Rüdiger Dillmann; Stefanie Speidel

doi:10.1117/12.2216750

18 March 2016 Superpixel-based structure classification for laparoscopic surgery

Sebastian Bodenstedt, Jochen Görtler, Martin Wagner, Hannes Kenngott, Beat Peter Müller-Stich, Rüdiger Dillmann, Stefanie Speidel

Author Affiliations +

Proceedings Volume 9786, Medical Imaging 2016: Image-Guided Procedures, Robotic Interventions, and Modeling; 978618 (2016) https://doi.org/10.1117/12.2216750
Event: SPIE Medical Imaging, 2016, San Diego, California, United States

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.

Citation Download Citation

Sebastian Bodenstedt, Jochen Görtler, Martin Wagner, Hannes Kenngott, Beat Peter Müller-Stich, Rüdiger Dillmann, and Stefanie Speidel "Superpixel-based structure classification for laparoscopic surgery", Proc. SPIE 9786, Medical Imaging 2016: Image-Guided Procedures, Robotic Interventions, and Modeling, 978618 (18 March 2016); https://doi.org/10.1117/12.2216750

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