Patients potentially suffer and are exposed to danger during invasive bronchoscopic diagnosis and surgery. In order to reduce this hazardous risk, we have developed an interactive virtual environment for the simulation of bronchoscopy (in short, called "virtual bronchoscopy"). Because of this state-of-the-art application, medical doctors can now obtain pre-operative information and perform pilot examinations in a virtual environment without any invasive or needless surgery. This 3D lung volume data of the patient is first acquired from CT and/or MRI scanning, without any pain being inflicted upon the patient. Then a vessel-tracking process is used to extract the patient's bronchial tree from the data. It is important to note that while manual tracking is tedious and labor-intensive, fully automatic tracking may not be as reliable in such a critical medical application. Thus a semi-automatic tracking technique called the Intelligent Path Tracker, which provides automation and sufficient user control during the tracking process, is most useful. This methodology is applied to a virtual bronchoscopy session, where doctors can use a 3D pen input device to navigate and visualize the bronchial tree of patients in a natural and interactive manner. To support an interactive frame rate, we also propose a new volume rendering acceleration technique, named IsoRegion Leaping. Through this technique visualization is further accelerated using a distributed rendering process based upon a TCP/IP network of low-cost PCs. Combining these approaches enables a 256x256x256 volumetric data representation of a human lung to be navigated and visualized at a frame rate of over 10 Hz in our virtual bronchoscopy system.
This paper proposes a discrete recurrent neural network model to implement winnertake-all function. This network model has simple organizations and clear dynamic behaviours. The dynamic properties of the proposed winner-take-all networks are studied in detail. Simulation results are given to show network performance. Since the network model is formulated as discrete time systems, it has advantages for computer simulations over digital simulations of continuous time neural network model. Thus they can be easily implemented in digital hardware. Index Terms | Winner-take-all neural networks, discrete recurrent neural networks, network response time.
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