Automatic classification of minimally invasive instruments based on endoscopic image sequences

Speidel, Stefanie; Benzko, Julia; Krappe, Sebastian; Sudra, Gunther; Azad, Pedram; Müller‐Stich, Beat P.; Gutt, Carsten N.; Dillmann, Rüdiger

doi:10.1117/12.811112

Cited by 38 publications

(30 citation statements)

References 2 publications

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“…Furthermore, by using soft computing methods, namely fuzzy logics, to analyze the incoming data, we have achieved enough robustness to produce useable result even with distorted distance information provided by imperfect soft tissue tracking. In addition to registering techniques, methods described in [27] can be used to identify instruments and surgical activities via image analysis algorithms.…”

Section: Discussionmentioning

confidence: 99%

Context-aware Augmented Reality in laparoscopic surgery

Katić

Wekerle

Görtler

et al. 2013

Computerized Medical Imaging and Graphics

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a b s t r a c tAugmented Reality is a promising paradigm for intraoperative assistance. Yet, apart from technical issues, a major obstacle to its clinical application is the man-machine interaction. Visualization of unnecessary, obsolete or redundant information may cause confusion and distraction, reducing usefulness and acceptance of the assistance system.We propose a system capable of automatically filtering available information based on recognized phases in the operating room. Our system offers a specific selection of available visualizations which suit the surgeon's needs best. The system was implemented for use in laparoscopic liver and gallbladder surgery and evaluated in phantom experiments in conjunction with expert interviews.

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Section: Discussionmentioning

confidence: 99%

Context-aware Augmented Reality in laparoscopic surgery

Katić

Wekerle

Görtler

et al. 2013

Computerized Medical Imaging and Graphics

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“…In terms of visual analysis, a classification of the full frame can be carried out to detect the presence of instruments [183]. The next level is to determine the position of the instrument in the two-dimensional image, or more specifically the position of the instrument tip, which is the main differentiation characteristic between different types of instruments [213]. Also for instrument tracking, the position of the tip is usually considered as reference point.…”

Section: Instrument Detection and Trackingmentioning

confidence: 99%

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

Münzer

Schoeffmann

Böszörményi

2017

Multimed Tools Appl

112

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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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“…There are several ways to deal with this problem. One way is to design classifiers that detect certain instruments or aspects of a surgery as for example done by [4,5]. When developing such a classifier, the feature space is usually reduced by manually choosing features that work well for a certain instrument.…”

Section: Signalsmentioning

confidence: 99%

“…This data has been used to classify four different states. In [5] five different laparoscopic tools have been recognized based on color and shape using a stereo endoscope in a simulated setup. Both methods have not been used on whole surgeries, where a lot of different instruments are used that often only have subtle differences.…”

Section: Introductionmentioning

confidence: 99%

Modeling and Segmentation of Surgical Workflow from Laparoscopic Video

Blum

Feußner

Navab

2010

Lecture Notes in Computer Science

126

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Abstract. Modeling and analyzing surgeries based on signals that are obtained automatically from the operating room (OR) is a field of recent interest. It can be valuable for analyzing and understanding surgical workflow, for skills evaluation and developing context-aware ORs. In minimally invasive surgery, laparoscopic video is easy to record but it is challenging to extract meaningful information from it. We propose a method that uses additional information about tool usage to perform a dimensionality reduction on image features. Using Canonical Correlation Analysis (CCA) a projection of a high-dimensional image feature space to a low dimensional space is obtained such that semantic information is extracted from the video. To model a surgery based on the signals in the reduced feature space two different statistical models are compared. The capability of segmenting a new surgery into phases only based on the video is evaluated. Dynamic Time Warping which strongly depends on the temporal order in combination with CCA shows the best results.

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Automatic classification of minimally invasive instruments based on endoscopic image sequences

Cited by 38 publications

References 2 publications

Context-aware Augmented Reality in laparoscopic surgery

Context-aware Augmented Reality in laparoscopic surgery

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

Modeling and Segmentation of Surgical Workflow from Laparoscopic Video

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