Superpixel-based structure classification for laparoscopic surgery
Publikation: Beitrag in Buch/Konferenzbericht/Sammelband/Gutachten › Beitrag in Konferenzband › Beigetragen › Begutachtung
Beitragende
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.
Details
Originalsprache | Englisch |
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Titel | Medical Imaging 2016 |
Redakteure/-innen | Robert J. Webster, Ziv R. Yaniv |
Herausgeber (Verlag) | SPIE - The international society for optics and photonics, Bellingham |
ISBN (elektronisch) | 9781510600218 |
Publikationsstatus | Veröffentlicht - 2016 |
Peer-Review-Status | Ja |
Extern publiziert | Ja |
Publikationsreihe
Reihe | Progress in Biomedical Optics and Imaging - Proceedings of SPIE |
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Band | 9786 |
ISSN | 1605-7422 |
Konferenz
Titel | Medical Imaging 2016: Image-Guided Procedures, Robotic Interventions, and Modeling |
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Dauer | 28 Februar - 1 März 2016 |
Stadt | San Diego |
Land | USA/Vereinigte Staaten |
Externe IDs
ORCID | /0000-0002-4590-1908/work/163294029 |
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Schlagworte
ASJC Scopus Sachgebiete
Schlagwörter
- Endoscopic image analysis, Endoscopic image segmentation, Instrument detection, Laparoscopic surgery, Superpixel classification, Tissue classification