15921507
Automatic Detection and Segmentation of Robot-Assisted Surgical Motions
Robotic surgical systems such as Intuitive Surgical's da Vinci system provide a rich source of motion and video data from surgical procedures. In principle, this data can be used to evaluate surgical skill, provide surgical training feedback, or document essential aspects of a procedure. If processed online, the data can be used to provide context-specific information or motion enhancements to the surgeon. However, in every case, the key step is to relate recorded motion data to a model of the procedure being performed. This paper examines our progress at developing techniques for "parsing" raw motion data from a surgical task into a labelled sequence of surgical gestures. Our current techniques have achieved >90% fully automated recognition rates on 15 datasets.
7435104
Navigating inner space: 3-D assistance for minimally invasive surgery
Abstract Since its inception about three decades ago, modern minimally invasive surgery has made huge advances in both technique and technology. However, the minimally invasive surgeon is still faced with daunting challenges in terms of visualization and hand-eye coordination. At the Center for Computer Integrated Surgical Systems and Technology (CISST) we have been developing a set of techniques for assisting surgeons in navigating and manipulating the three-dimensional space within the human body. In order to develop such systems, a variety of challenging visual tracking, reconstruction and registration problems must be solved. In addition, this information must be tied to methods for assistance that improve surgical accuracy and reliability but allow the surgeon to retain ultimate control of the procedure and do not prolong time in the operating room. In this article, we present two problem areas, eye microsurgery and thoracic minimally invasive surgery, where computational vision can play a role. We then describe methods we have developed to process video images for relevant geometric information, and related control algorithms for providing interactive assistance. Finally, we present results from implemented systems.
207032999
Power and Type I error rates of goodness-of-fit statistics for binomial generalized estimating equations (GEE) models
Binary outcomes are very common in medical studies. Logistic regression is typically used to analyze independent binary outcomes while generalized estimating equations regression methods (GEE) are often used to analyze correlated binary data. Several goodness-of-fit (GoF) statistics for the GEE methods have been developed recently. The objective of this study is to compare the power and Type I error rates of existing GEE GoF statistics using simulated data under different conditions. The number of clusters was varied in each condition. Different tested models included discrete, continuous, observation-specific and/or cluster-specific covariates. Two or three observations per cluster were generated with various correlations between observations. No single GEE GoF statistic performed best across all conditions. Generally, the larger the number of clusters, the more powerful the GEE GoF statistics. The GEE GoF statistics with correctly specified working correlation matrices tended to be robust in terms of Type I error rates and more powerful. For data with two observations per cluster, both Evans and Pan's statistics [1998. Goodness of fit in two models for clustered binary data. Ph.D. Dissertation, University of Massachusetts; 2002a. Goodness-of-fit tests for GEE with correlated binary data. Scand. J. Stat. 29(1), 101-110.] and Barnhart-Williamson's statistics [1998. Goodness-of-fit tests for GEE modeling with binary data. Biometrics 54, 720-729.] performed well for detecting the effect of the omitted interaction between two binary covariates. Barnhart-Williamson's statistics were generally the most powerful for detecting other types of interactions in models with at least one continuous covariate. For data with three observations per cluster, Evans and Pan's statistics performed best. fit in two models for clustered binary data. Ph.D. Dissertation, University of Massachusetts; 2002a. Goodness-of-fit tests for GEE with correlated binary data. Scand. J. Stat. 29(1), 101-110.] statistics performed best.
1232058
CORNet: A Scalable and Bandwidth-Efficient Optical Burst Switching Ring Architecture for Metro Area Networks
The rapid evolution of networks has been driven by new advances in enabling technologies and the explosive growth of Internet traffic. optical burst switching (OBS) is not only cost-effective, but also provides high throughput, high bandwidth utilization, and low transmission latency. These benefits make OBS a viable solution for next-generation metro area networks (MANs) in which the traffic models are changed more dramatically than today's networks. Based on wavelength division multiplexing (WDM) technology, this study presents a loss-free optical burst switching metro ring architecture, designated the collision-free optic-burst ring network (CORNet). The proposed architecture ensures a collision-free transmission of data bursts, while retaining the advantage of the statistical multiplexing provided by conventional OBS networks. The simulation results confirm the effectiveness of the proposed CORNet architecture
32016448
Proof systems for message-passing process algebras
We give sound and complete proof systems for a variety of bisimulation based equivalences over a message-passing process algebra. The process algebra is a generalisation of pureCCS where the actions consist of receiving and sending messages or data on communication channels; the standard prefixing operatora.p is replaced by the two operatorsc?x.p andc!e.p and in addition messages can be tested by a conditional construct. The various proof systems are parameterised on auxiliary proof systems for deciding on equalities or more general boolean identities over the expression language for data. The completeness of these proof systems are thus relative to the completeness of the auxiliary proof systems.
14295911
Unique fixpoint induction for message-passing process calculi
Abstract We present a proof system for message-passing process calculi with recursion. The key inference rule to deal with recursive processes is a version of Unique Fixpoint Induction for process abstractions. We prove that the proof system is sound and also complete for guarded regular message-passing processes. We also show that the system is incomplete for unguarded processes and discuss more powerful extensions with inductive inference rules.
27608466
Symbolic Bisimulations