The Experts below are selected from a list of 57807 Experts worldwide ranked by ideXlab platform
Keith C. C. Chan - One of the best experts on this subject based on the ideXlab platform.
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Workflow Analysis for web publishing using a stage activity process model
Journal of Systems and Software, 2005Co-Authors: Catherine Y P Chan, Keith C. C. ChanAbstract:Web publishing has been the core business of ICP (Internet Content Provider) companies. It is also a major functional component in other Internet industry sectors. However, although there have been on-going developments in this area, up to our knowledge, very little work has been done on web publishing process models and methodologies. In this paper we describe a general process model for web publishing based on Workflow Analysis. Using a case study approach, the existing web publishing methods used by major Hong Kong ICP companies have been evaluated. Based on the evaluation results and our observations, the issues and problems of the existing methods are identified. A general process model, called the stage-activity model, is proposed which facilitates the development of a framework for automating the web publishing process with the incorporation of Workflow management. The results of this research contribute to a clear understanding of what challenges ICPs are currently facing and a solution for building an automating web publishing system. The framework can also be used to evaluate and select the web publishing tools in the market.
Nassir Navab - One of the best experts on this subject based on the ideXlab platform.
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random forests for phase detection in surgical Workflow Analysis
International Conference Information Processing, 2014Co-Authors: Ralf Stauder, H Feussner, Asli Okur, Loic Peter, Armin Schneider, Michael Kranzfelder, Nassir NavabAbstract:Identifying and recognizing the Workflow of surgical interventions is a field of growing interest. Several methods have been developed to identify intra-operative activities, detect common phases in the surgical Workflow and combine the gained knowledge into Surgical Process Models. Numerous applications of this knowledge are conceivable, from semi-automatic report generation, teaching and objective surgeon evaluation to context-aware operating rooms and simulation of interventions to optimize the operating room layout.
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IPCAI - Random Forests for Phase Detection in Surgical Workflow Analysis
Information Processing in Computer-Assisted Interventions, 2014Co-Authors: Ralf Stauder, Asli Okur, Loic Peter, Armin Schneider, Michael Kranzfelder, Hubertus Feussner, Nassir NavabAbstract:Identifying and recognizing the Workflow of surgical interventions is a field of growing interest. Several methods have been developed to identify intra-operative activities, detect common phases in the surgical Workflow and combine the gained knowledge into Surgical Process Models. Numerous applications of this knowledge are conceivable, from semi-automatic report generation, teaching and objective surgeon evaluation to context-aware operating rooms and simulation of interventions to optimize the operating room layout.
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a boosted segmentation method for surgical Workflow Analysis
Medical Image Computing and Computer-Assisted Intervention, 2007Co-Authors: Nicolas Padoy, Tobias Blum, Irfan Essa, H Feussner, Mo Berger, Nassir NavabAbstract:As demands on hospital efficiency increase, there is a stronger need for automatic Analysis, recovery, and modification of surgical Workflows. Even though most of the previous work has dealt with higher level and hospital-wide Workflow including issues like document management, Workflow is also an important issue within the surgery room. Its study has a high potential, e.g., for building context-sensitive operating rooms, evaluating and training surgical staff, optimizing surgeries and generating automatic reports. In this paper we propose an approach to segment the surgical Workflow into phases based on temporal synchronization of multidimensional state vectors. Our method is evaluated on the example of laparoscopic cholecystectomy with state vectors representing tool usage during the surgeries. The discriminative power of each instrument in regard to each phase is estimated using AdaBoost. A boosted version of the Dynamic Time Warping (DTW) algorithm is used to create a surgical reference model and to segment a newly observed surgery. Full cross-validation on ten surgeries is performed and the method is compared to standard DTW and to Hidden Markov Models.
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MICCAI (1) - A boosted segmentation method for surgical Workflow Analysis
Medical Image Computing and Computer-Assisted Intervention – MICCAI 2007, 2007Co-Authors: Nicolas Padoy, Tobias Blum, Irfan Essa, Mo Berger, Hubertus Feussner, Nassir NavabAbstract:As demands on hospital efficiency increase, there is a stronger need for automatic Analysis, recovery, and modification of surgical Workflows. Even though most of the previous work has dealt with higher level and hospital-wide Workflow including issues like document management, Workflow is also an important issue within the surgery room. Its study has a high potential, e.g., for building context-sensitive operating rooms, evaluating and training surgical staff, optimizing surgeries and generating automatic reports. In this paper we propose an approach to segment the surgical Workflow into phases based on temporal synchronization of multidimensional state vectors. Our method is evaluated on the example of laparoscopic cholecystectomy with state vectors representing tool usage during the surgeries. The discriminative power of each instrument in regard to each phase is estimated using AdaBoost. A boosted version of the Dynamic Time Warping (DTW) algorithm is used to create a surgical reference model and to segment a newly observed surgery. Full cross-validation on ten surgeries is performed and the method is compared to standard DTW and to Hidden Markov Models.
Catherine Y P Chan - One of the best experts on this subject based on the ideXlab platform.
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Workflow Analysis for web publishing using a stage activity process model
Journal of Systems and Software, 2005Co-Authors: Catherine Y P Chan, Keith C. C. ChanAbstract:Web publishing has been the core business of ICP (Internet Content Provider) companies. It is also a major functional component in other Internet industry sectors. However, although there have been on-going developments in this area, up to our knowledge, very little work has been done on web publishing process models and methodologies. In this paper we describe a general process model for web publishing based on Workflow Analysis. Using a case study approach, the existing web publishing methods used by major Hong Kong ICP companies have been evaluated. Based on the evaluation results and our observations, the issues and problems of the existing methods are identified. A general process model, called the stage-activity model, is proposed which facilitates the development of a framework for automating the web publishing process with the incorporation of Workflow management. The results of this research contribute to a clear understanding of what challenges ICPs are currently facing and a solution for building an automating web publishing system. The framework can also be used to evaluate and select the web publishing tools in the market.
Sebastian Bodenstedt - One of the best experts on this subject based on the ideXlab platform.
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Active learning using deep Bayesian networks for surgical Workflow Analysis.
International Journal of Computer Assisted Radiology and Surgery, 2019Co-Authors: Sebastian Bodenstedt, Dominik Rivoir, Alexander Jenke, Martin Wagner, Michael Breucha, Beat P. Müller-stich, Sören Torge Mees, Jürgen Weitz, Stefanie SpeidelAbstract:PURPOSE For many applications in the field of computer-assisted surgery, such as providing the position of a tumor, specifying the most probable tool required next by the surgeon or determining the remaining duration of surgery, methods for surgical Workflow Analysis are a prerequisite. Often machine learning-based approaches serve as basis for analyzing the surgical Workflow. In general, machine learning algorithms, such as convolutional neural networks (CNN), require large amounts of labeled data. While data is often available in abundance, many tasks in surgical Workflow Analysis need annotations by domain experts, making it difficult to obtain a sufficient amount of annotations. METHODS The aim of using active learning to train a machine learning model is to reduce the annotation effort. Active learning methods determine which unlabeled data points would provide the most information according to some metric, such as prediction uncertainty. Experts will then be asked to only annotate these data points. The model is then retrained with the new data and used to select further data for annotation. Recently, active learning has been applied to CNN by means of deep Bayesian networks (DBN). These networks make it possible to assign uncertainties to predictions. In this paper, we present a DBN-based active learning approach adapted for image-based surgical Workflow Analysis task. Furthermore, by using a recurrent architecture, we extend this network to video-based surgical Workflow Analysis. To decide which data points should be labeled next, we explore and compare different metrics for expressing uncertainty. RESULTS We evaluate these approaches and compare different metrics on the Cholec80 dataset by performing instrument presence detection and surgical phase segmentation. Here we are able to show that using a DBN-based active learning approach for selecting what data points to annotate next can significantly outperform a baseline based on randomly selecting data points. In particular, metrics such as entropy and variation ratio perform consistently on the different tasks. CONCLUSION We show that using DBN-based active learning strategies make it possible to selectively annotate data, thereby reducing the required amount of labeled training in surgical Workflow-related tasks.
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Active Learning using Deep Bayesian Networks for Surgical Workflow Analysis
arXiv: Computer Vision and Pattern Recognition, 2018Co-Authors: Sebastian Bodenstedt, Dominik Rivoir, Alexander Jenke, Martin Wagner, Michael Breucha, Beat P. Müller-stich, Sören Torge Mees, Jürgen Weitz, Stefanie SpeidelAbstract:For many applications in the field of computer assisted surgery, such as providing the position of a tumor, specifying the most probable tool required next by the surgeon or determining the remaining duration of surgery, methods for surgical Workflow Analysis are a prerequisite. Often machine learning based approaches serve as basis for surgical Workflow Analysis. In general machine learning algorithms, such as convolutional neural networks (CNN), require large amounts of labeled data. While data is often available in abundance, many tasks in surgical Workflow Analysis need data annotated by domain experts, making it difficult to obtain a sufficient amount of annotations. The aim of using active learning to train a machine learning model is to reduce the annotation effort. Active learning methods determine which unlabeled data points would provide the most information according to some metric, such as prediction uncertainty. Experts will then be asked to only annotate these data points. The model is then retrained with the new data and used to select further data for annotation. Recently, active learning has been applied to CNN by means of Deep Bayesian Networks (DBN). These networks make it possible to assign uncertainties to predictions. In this paper, we present a DBN-based active learning approach adapted for image-based surgical Workflow Analysis task. Furthermore, by using a recurrent architecture, we extend this network to video-based surgical Workflow Analysis. We evaluate these approaches on the Cholec80 dataset by performing instrument presence detection and surgical phase segmentation. Here we are able to show that using a DBN-based active learning approach for selecting what data points to annotate next outperforms a baseline based on randomly selecting data points.
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OR 2.0/CARE/CLIP/ISIC@MICCAI - Temporal Coherence-based Self-supervised Learning for Laparoscopic Workflow Analysis
Lecture Notes in Computer Science, 2018Co-Authors: Isabel Funke, Alexander Jenke, Sören Torge Mees, Jürgen Weitz, Stefanie Speidel, Sebastian BodenstedtAbstract:In order to provide the right type of assistance at the right time, computer-assisted surgery systems need context awareness. To achieve this, methods for surgical Workflow Analysis are crucial. Currently, convolutional neural networks provide the best performance for video-based Workflow Analysis tasks. For training such networks, large amounts of annotated data are necessary. However, collecting a sufficient amount of data is often costly, time-consuming, and not always feasible. In this paper, we address this problem by presenting and comparing different approaches for self-supervised pretraining of neural networks on unlabeled laparoscopic videos using temporal coherence. We evaluate our pretrained networks on Cholec80, a publicly available dataset for surgical phase segmentation, on which a maximum \(F_1\) score of 84.6 was reached. Furthermore, we were able to achieve an increase of the \(F_1\) score of up to 10 points when compared to a non-pretrained neural network.
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temporal coherence based self supervised learning for laparoscopic Workflow Analysis
OR 2.0 CARE CLIP ISIC@MICCAI, 2018Co-Authors: Isabel Funke, Alexander Jenke, Sören Torge Mees, Jürgen Weitz, Stefanie Speidel, Sebastian BodenstedtAbstract:In order to provide the right type of assistance at the right time, computer-assisted surgery systems need context awareness. To achieve this, methods for surgical Workflow Analysis are crucial. Currently, convolutional neural networks provide the best performance for video-based Workflow Analysis tasks. For training such networks, large amounts of annotated data are necessary. However, collecting a sufficient amount of data is often costly, time-consuming, and not always feasible. In this paper, we address this problem by presenting and comparing different approaches for self-supervised pretraining of neural networks on unlabeled laparoscopic videos using temporal coherence. We evaluate our pretrained networks on Cholec80, a publicly available dataset for surgical phase segmentation, on which a maximum \(F_1\) score of 84.6 was reached. Furthermore, we were able to achieve an increase of the \(F_1\) score of up to 10 points when compared to a non-pretrained neural network.
Oliver Burgert - One of the best experts on this subject based on the ideXlab platform.
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Workflow Analysis of laparoscopic Nissen fundoplication in infant pigs- a model for surgical feedback and training.
Journal of laparoendoscopic & advanced surgical techniques. Part A, 2009Co-Authors: Alexandra Krauss, Oliver J. Muensterer, Thomas Neumuth, Robin Wachowiak, Bernd Donaubauer, Werner Korb, Oliver BurgertAbstract:Many fields use Workflow Analysis to assess and improve performance of complex tasks. In pediatric endosurgery, Workflow Analysis may help optimize operative planning and motor skills by breaking down the procedure into particular phases, evaluating these steps individually, and supplying feedback to the surgeon. To develop a module of computer-based surgical Workflow Analysis for laparoscopic Nissen fundoplication(LNF) and to evaluate its applicability in an infant pig model. LNF was performed in 12 pigs (weight, 7-10 kg) by a single surgeon. Based on synchronized intra and extracorporal movie recordings, the surgical Workflow was segmented into temporal operative phases(preparation, dissection, reconstruction and conclusion). During each stage, all actions were recorded in a virtual timeline using a customized Workflow editor. Specific tasks, such as knot-tying, were evaluated in detail.Time necessary to perform these actions was compared throughout the study. While time required for the preparation decreased by more than 70% from 4577 to 1379 seconds,and the dissection phase decreased from 2359 to 399 seconds (pig 1 and 12, respectively), the other two phases remained relatively stable. Mean time to perform the entire suture and a 5-throw knot remained constant as well. Our Workflow Analysis model allows the quantitative evaluation of dynamic actions related to LNF.This data can be used to define average benchmark criteria for the procedures that comprise this operation. It thereby permits task-oriented refinement of surgical technique as well as monitoring the efficacy of training.Although preoperative preparation time decreased substantially, and dissection became faster, time required for the reconstruction and conclusion phases remained relatively constant for a surgeon with moderate experience.Likewise, knot-tying did not accelerate in this setting.S-117
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Workflow Analysis of laparoscopic Nissen fundoplication in infant pigs- a model for surgical feedback and training.
Journal of Laparoendoscopic & Advanced Surgical Techniques, 2008Co-Authors: Alexandra Krauss, Oliver J. Muensterer, Thomas Neumuth, Robin Wachowiak, Bernd Donaubauer, Werner Korb, Oliver BurgertAbstract:Abstract Background: Many fields use Workflow Analysis to assess and improve performance of complex tasks. In pediatric endosurgery, Workflow Analysis may help optimize operative planning and motor skills by breaking down the procedure into particular phases, evaluating these steps individually, and supplying feedback to the surgeon. Objective: To develop a module of computer-based surgical Workflow Analysis for laparoscopic Nissen fundoplication (LNF) and to evaluate its applicability in an infant pig model. Methods: LNF was performed in 12 pigs (weight, 7–10 kg) by a single surgeon. Based on synchronized intra- and extracorporal movie recordings, the surgical Workflow was segmented into temporal operative phases (preparation, dissection, reconstruction and conclusion). During each stage, all actions were recorded in a virtual timeline using a customized Workflow editor. Specific tasks, such as knot-tying, were evaluated in detail. Time necessary to perform these actions was compared throughout the study...
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data warehousing technology for surgical Workflow Analysis
Computer-Based Medical Systems, 2008Co-Authors: Thomas Neumuth, Svetlana Mansmann, Marc H Scholl, Oliver BurgertAbstract:Analysis of surgical procedures is an emerging domain of medical engineering aimed at advancing surgical assist systems. The term Surgical Workflows is used for describing the methodical framework for acquiring formal descriptions of surgical interventions. A formal model and a uniform recording scheme of a surgical process are crucial for systematic accumulation of relevant data from running surgical instances in a form, appropriate for Analysis. This paper describes the process of designing a data warehouse for surgical Workflow Analysis. Data warehousing technology, originally developed for quantitative business data Analysis for the purpose of decision making, is adaptable to the requirements of surgical data and Analysis. We describe a conceptual model of a surgical procedure obtained in accordance with the multidimensional data model. We demonstrate how a subject-oriented multidimensional perspective of a surgery and its components enables powerful Analysis and exploration by defining the metrics of interests and aggregating those metrics along various dimensions and levels of details. Apart from its primary function, i.e. quantitative Analysis, the data of the surgical data warehouse may serve as input for other data-intensive systems, e.g. visualization and data mining tools.
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CBMS - Data Warehousing Technology for Surgical Workflow Analysis
2008 21st IEEE International Symposium on Computer-Based Medical Systems, 2008Co-Authors: Thomas Neumuth, Svetlana Mansmann, Marc H Scholl, Oliver BurgertAbstract:Analysis of surgical procedures is an emerging domain of medical engineering aimed at advancing surgical assist systems. The term Surgical Workflows is used for describing the methodical framework for acquiring formal descriptions of surgical interventions. A formal model and a uniform recording scheme of a surgical process are crucial for systematic accumulation of relevant data from running surgical instances in a form, appropriate for Analysis. This paper describes the process of designing a data warehouse for surgical Workflow Analysis. Data warehousing technology, originally developed for quantitative business data Analysis for the purpose of decision making, is adaptable to the requirements of surgical data and Analysis. We describe a conceptual model of a surgical procedure obtained in accordance with the multidimensional data model. We demonstrate how a subject-oriented multidimensional perspective of a surgery and its components enables powerful Analysis and exploration by defining the metrics of interests and aggregating those metrics along various dimensions and levels of details. Apart from its primary function, i.e. quantitative Analysis, the data of the surgical data warehouse may serve as input for other data-intensive systems, e.g. visualization and data mining tools.
