Workflow analysis and surgical phase recognition in minimally invasive surgery

Weede, Oliver; Dittrich, Frank; Wörn, Heinz; Jensen, Brian; Knoll, Alois; Wilhelm, Dirk; Kranzfelder, Michael; Schneider, Armin; Feußner, H.

doi:10.1109/robio.2012.6491111

Cited by 30 publications

(18 citation statements)

References 12 publications

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“…Including the prediction of the end-effectors' movement for computing the camera position, leads to 29.2% less camera movements and to an improved visibility of the instruments [19]. The recognition rate of the surgical phases in a sigma resection is 93% [16]. The combination of the midterm prediction and the recognition of the surgical phases allows us to expand the prediction to a whole intervention preserving reliability.…”

Section: Resultsmentioning

confidence: 99%

“…This approach seems not generic. An overview of other approaches to recognize performed actions and surgical phases can be found in [16].…”

Section: Related Workmentioning

confidence: 99%

“…The coagulations and the recorded trajectories are then fused. Furthermore, the interventions are annotated by our medical partners relating to the ongoing surgical phase p. For example, the medical partners have identified nine surgical phases for the sigma resection and 13 phases for the rectum resection [16]. …”

Section: Workflow Of the Interventionmentioning

confidence: 99%

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Towards Cognitive Medical Robotics in Minimal Invasive Surgery

Weede

Bihlmaier

Hutzl

et al. 2013

Proceedings of Conference on Advances in Robotics

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Up to date, medical robots for minimal invasive surgery do not provide assistance appropriate to the workflow of the intervention. A simple concept of a cognitive system is presented, which is derived from a classic closed-loop control. As implementation, we present a cognitive medical robot system using lightweight robots with redundant kinematics. The robot system includes several control modes and human-machine interfaces. We focus on describing knowledge acquisition about the workflow of an intervention and present two example applications utilizing the acquired knowledge: autonomous camera guidance and planning of minimal invasive port (trocar) positions in combination with an initial robot setup. Port planning is described as optimization problem. The autonomous camera system includes a mid-term movement prediction of the ongoing intervention. The cognitive approach to a medical robot system includes taking the environment into account. The goal is to create a system that acts as a human assistant, who perceives the situation, understands the context based on his knowledge and acts appropriate.

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Section: Resultsmentioning

confidence: 99%

“…This approach seems not generic. An overview of other approaches to recognize performed actions and surgical phases can be found in [16].…”

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

Towards Cognitive Medical Robotics in Minimal Invasive Surgery

Weede

Bihlmaier

Hutzl

et al. 2013

Proceedings of Conference on Advances in Robotics

Self Cite

View full text Add to dashboard Cite

show abstract

“…However, metadata obtained from video analysis is only one of many possible inputs for surgical situation understanding systems proposed in the literature. Often, various additional sensor data are used, e.g., weight of the irrigation and suction bags, the intraabdominal CO 2 pressure and the inclination of the surgical table [221] or a coagulation audio signal [262]. The focus in this research area is not on how to obtain the required information from the video, but how to map the available signals (e.g., binary information about instrument presence) to the corresponding surgical phase.…”

Section: Context Awarenessmentioning

confidence: 99%

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

Münzer

Schoeffmann

Böszörményi

2017

Multimed Tools Appl

113

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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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“…Most of these approaches achieve accuracies of between 80% and 90%. Additionally, this research, in general, focused on several different clinical use-cases, such as pituitary surgery [33], laparoscopic sigmoidectomy [50], cataract surgery [51,52], or laparoscopic cholecystectomy [36].…”

Section: Data Acquisition By Sensor Systemsmentioning

confidence: 99%

Surgical process modeling

Neumuth

2017

Innovative Surgical Sciences

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Due to the rapidly evolving medical, technological, and technical possibilities, surgical procedures are becoming more and more complex. On the one hand, this offers an increasing number of advantages for patients, such as enhanced patient safety, minimal invasive interventions, and less medical malpractices. On the other hand, it also heightens pressure on surgeons and other clinical staff and has brought about a new policy in hospitals, which must rely on a great number of economic, social, psychological, qualitative, practical, and technological resources. As a result, medical disciplines, such as surgery, are slowly merging with technical disciplines. However, this synergy is not yet fully matured. The current information and communication technology in hospitals cannot manage the clinical and operational sequence adequately. The consequences are breaches in the surgical workflow, extensions in procedure times, and media disruptions. Furthermore, the data accrued in operating rooms (ORs) by surgeons and systems are not sufficiently implemented. A flood of information, "big data", is available from information systems. That might be deployed in the context of Medicine 4.0 to facilitate the surgical treatment. However, it is unused due to infrastructure breaches or communication errors. Surgical process models (SPMs) alleviate these problems. They can be defined as simplified, formal, or semiformal representations of a network of surgery-related activities, reflecting a predefined subset of interest. They can employ different means of generation, languages, and data acquisition strategies. They can represent surgical interventions with high resolution, offering qualifiable and quantifiable information on the course of the intervention on the level of single, minute, surgical work-steps. The basic idea is to gather information concerning the surgical intervention and its activities, such as performance time, surgical instrument used, trajectories, movements, or intervention phases. These data can be gathered by means of workflow recordings.These recordings are abstracted to represent an individual surgical process as a model and are an essential requirement to enable Medicine 4.0 in the OR. Further abstraction can be generated by merging individual process models to form generic SPMs to increase the validity for a larger number of patients. Furthermore, these models can be applied in a wide variety of use-cases. In this regard, the term "modeling" can be used to support either one or more of the following tasks: "to describe", "to understand", "to explain", to optimize", "to learn", "to teach", or "to automate". Possible use-cases are requirements analyses, evaluating surgical assist systems, generating surgeon-specific training-recommendation, creating workflow management systems for ORs, and comparing different surgical strategies. The presented chapter will give an introduction into this challenging topic, presenting different methods to generate SPMs from the workflow in the OR, as well as various...

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Workflow analysis and surgical phase recognition in minimally invasive surgery

Cited by 30 publications

References 12 publications

Towards Cognitive Medical Robotics in Minimal Invasive Surgery

Towards Cognitive Medical Robotics in Minimal Invasive Surgery

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

Surgical process modeling

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