Context-aware Augmented Reality in laparoscopic surgery

Katić, Darko; Wekerle, Anna-Laura; Görtler, Jochen; Spengler, Patrick; Bodenstedt, Sebastian; Röhl, Sebastian; Suwelack, Stefan; Kenngott, Hannes; Wagner, Martin; Müller‐Stich, Beat P.; Dillmann, Rüdiger; Speidel, Stefanie

doi:10.1016/j.compmedimag.2013.03.003

Cited by 60 publications

(38 citation statements)

References 22 publications

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“…It was further sponsored by the European Social Fund of the State Baden-Wuerttemberg and the Klaus Tschira Foundation. challenges in this context is the image-based tracking of medical instruments in the endoscopic images, which is a prerequisite for surgical navigation [1], skill assessment [2] and workflow analysis [3], for example. State-of-the-art methods apply machine learning techniques to learn the shape and appearance of different objects from labeled training data [4].…”

Section: Introductionmentioning

confidence: 99%

Can Masses of Non-Experts Train Highly Accurate Image Classifiers?

Maier-Hein

Mersmann

Kondermann

et al. 2014

Medical Image Computing and Computer-Assisted Intervention – MICCAI 2014

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Machine learning algorithms are gaining increasing interest in the context of computer-assisted interventions. One of the bottlenecks so far, however, has been the availability of training data, typically generated by medical experts with very limited resources. Crowdsourcing is a new trend that is based on outsourcing cognitive tasks to many anonymous untrained individuals from an online community. In this work, we investigate the potential of crowdsourcing for segmenting medical instruments in endoscopic image data. Our study suggests that (1) segmentations computed from annotations of multiple anonymous non-experts are comparable to those made by medical experts and (2) training data generated by the crowd is of the same quality as that annotated by medical experts. Given the speed of annotation, scalability and low costs, this implies that the scientific community might no longer need to rely on experts to generate reference or training data for certain applications.

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Section: Introductionmentioning

confidence: 99%

Can Masses of Non-Experts Train Highly Accurate Image Classifiers?

Maier-Hein

Mersmann

Kondermann

et al. 2014

Medical Image Computing and Computer-Assisted Intervention – MICCAI 2014

Self Cite

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“…The goal should be to automatically select the appropriate assistance for the current state of the procedure in a context-aware manner, providing hints or situation-specific additional information. Such assistance can also go beyond visualizations of the pre-operative model, e.g., it can support decision making by finding situations similar to the current one and showing how other surgeons handled a similar exceptional situation [95]. Another use case is to simulate the effect of a surgical step without actually executing it [115].…”

Section: Context Awarenessmentioning

confidence: 99%

“…A fundamentally different approach is to use formal knowledge representation methods like ontologies that use rules and logical reasoning to derive the current state. For example, Katic et al use Description Logic in the OWL-standard (Web Ontology Language) [94,95,214].…”

Section: Context Awarenessmentioning

confidence: 99%

“…On a higher abstraction level, a procedure is subdivided into pre-defined operation phases that describe the typical sequence of a surgery. Existing approaches focus on well standardized procedures, in most cases cholecystectomy (removal of the gallbladder) [25,95,99,109,174], which can be broken down to distinct phases very well. Surgical workflow understanding is also of particular interest for post-procedural applications, especially for temporal video segmentation and summarization (see Section 4.2.3).…”

Section: Context Awarenessmentioning

confidence: 99%

See 1 more Smart Citation

Content-based processing and analysis of endoscopic images and videos: A survey

Münzer

Schoeffmann

Böszörményi

2017

Multimed Tools Appl

106

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In recent years, digital endoscopy has established as key technology for medical screenings and minimally invasive surgery. Since then, various research communities with manifold backgrounds have picked up on the idea of processing and automatically analyzing the inherently available video signal that is produced by the endoscopic camera. Proposed works mainly include image processing techniques, pattern recognition, machine learning methods and Computer Vision algorithms. While most contributions deal with realtime assistance at procedure time, the post-procedural processing of recorded videos is still in its infancy. Many post-processing problems are based on typical Multimedia methods like indexing, retrieval, summarization and video interaction, but have only been sparsely addressed so far for this domain. The goals of this survey are (1) to introduce this research field to a broader audience in the Multimedia community to stimulate further research, (2) to describe domain-specific characteristics of endoscopic videos that need to be addressed in a pre-processing step, and (3) to systematically bring together the very diverse research results for the first time to provide a broader overview of related research that is currently not perceived as belonging together.

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“…1 An assistance system requires knowledge about the surgical scene, such as the locations of surgical instruments, anatomical structures and maligned tissue. Such information can be visualized using augmented reality or it can be used for on-the-fly measurements.…”

Section: Introductionmentioning

confidence: 99%

Superpixel-based structure classification for laparoscopic surgery

Bodenstedt

Görtler

Wagner

et al. 2016

Medical Imaging 2016: Image-Guided Procedures, Robotic Interventions, and Modeling

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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.

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Context-aware Augmented Reality in laparoscopic surgery

Cited by 60 publications

References 22 publications

Can Masses of Non-Experts Train Highly Accurate Image Classifiers?

Can Masses of Non-Experts Train Highly Accurate Image Classifiers?

Content-based processing and analysis of endoscopic images and videos: A survey

Superpixel-based structure classification for laparoscopic surgery

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