Random Forests for Phase Detection in Surgical Workflow Analysis

Stauder, Ralf; Okur, Asli; Peter, Loic; Schneider, Armin; Kranzfelder, Michael; Feußner, H.; Navab, Nassir

doi:10.1007/978-3-319-07521-1_16

Cited by 52 publications

(37 citation statements)

References 11 publications

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“…They studied sensor significance in order to identity the most important features for surgical phase prediction. Stauder et al [21] used Random Forest (i.e., a bag of decision trees) to predict surgical phases from sensors measurement. Other models like hidden Markov model (HMM) were also considered by Padoy et al [22,23] for online recognition of surgical steps.…”

Section: Review Of Related Workmentioning

confidence: 99%

Semi-supervised spatio-temporal CNN for recognition of surgical workflow

Chen

Sun

Zhong

2018

J Image Video Proc.

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Robust and automated surgical workflow detection in real time is a core component of the future intelligent operating room. Based on this technology, it can help medical staff to automate and intelligently complete many routine activities during surgery. Recognition of surgical workflow based on traditional pattern recognition methods requires a large number of labeled surgical video data. However, the labeled surgical video data requires expert knowledge and it is difficult and time consuming to collect a sufficient number of labeled surgical video data in the medical field. Therefore, this paper proposes a semi-supervised spatio-temporal convolutional network for the recognition of surgical workflow based on convolutional neural networks and temporal-recursive networks. Firstly, we build a spatial convolutional extraction feature network based on unsupervised generative adversarial learning. Then, we build a bridge between lowlevel surgical video features and high-level surgical workflow semantics based on an unsupervised temporal-ordered network learning approach. Finally, we use the semi-supervised learning method to integrate the spatial model and the temporal model to fine-tune the network, and realize the intelligent recognition of the surgical workflow at a low cost to efficiently determine the progress of the surgical workflow. We performed some experiments for validating the mode based on m2cai16-workflow dataset. It shows that the proposed model can effectively extract the surgical feature and determine the surgical workflow. The Jaccard score of the model reaches 71.3%, and the accuracy of the model reaches 85.8%.

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Section: Review Of Related Workmentioning

confidence: 99%

Semi-supervised spatio-temporal CNN for recognition of surgical workflow

Chen

Sun

Zhong

2018

J Image Video Proc.

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“…To further abstract from raw signals, there are attempts to extract features for a more concise representation of surgical workflows. For the most part, established algorithms such as hidden Markov models (HMMs) [10,11], dynamic time warping [12], random forests (RFs) [13] or statistical analysis [14] are used and adapted. Combinations of different algorithms are also explored [15,16].…”

Section: Introductionmentioning

confidence: 99%

Bridging the gap between formal and experience-based knowledge for context-aware laparoscopy

et al. 2016

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“…Another frequently used method is Dynamic Time Warping (DTW) [58], which can also be used without explicit pre-defined models by temporally aligning surgeries of the same type [3]. Besides HMM and DTW also alternative methods like Random Forests have been proposed [221]. A fundamentally different approach is to use formal knowledge representation methods like ontologies that use rules and logical reasoning to derive the current state.…”

Section: Context Awarenessmentioning

confidence: 99%

“…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

114

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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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Random Forests for Phase Detection in Surgical Workflow Analysis

Cited by 52 publications

References 11 publications

Semi-supervised spatio-temporal CNN for recognition of surgical workflow

Semi-supervised spatio-temporal CNN for recognition of surgical workflow

Bridging the gap between formal and experience-based knowledge for context-aware laparoscopy

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

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