Preoperative planning systems are commonly used for oral implant surgery. One of the objectives is to determine if the quantity and quality of bone is sufficient to sustain an implant while avoiding critical anatomic structures. We aim to automate the segmentation of jaw tissues on CT images: cortical bone, trabecular core and especially the mandibular canal containing the dental nerve. This nerve must be avoided during implant surgery to prevent lip numbness. Previous work in this field used thresholds or filters and needed manual initialization. An automated system based on the use of Active Appearance Models (AAMs) is proposed. Our contribution is a completely automated segmentation of tissues and a semi-automatic landmarking process necessary to create the AAM model. The AAM is trained using 215 images and tested with a leave-4-out scheme. Results obtained show an initialization error of 3.25% and a mean error of 1.63mm for the cortical bone, 2.90mm for the trabecular core, 4.76mm for the mandibular canal and 3.40mm for the dental nerve.

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Abstract-Web prefetching is one of the techniques proposed to reduce user's perceived latencies in the World Wide Web. The spatial locality shown by user's accesses makes it possible to predict future accesses based on the previous ones. A prefetching engine uses these predictions to prefetch the web objects before the user demands them. The existing prediction algorithms achieved an acceptable performance when they were proposed but the high increase in the amount of embedded objects per page has reduced their effectiveness in the current web. In this paper we show that most of the predictions made by the existing algorithms are useless to reduce the user's perceived latency because these algorithms do not take into account how current web pages are structured, i.e., an HTML object with several embedded objects. Thus, they predict the accesses to the embedded objects in an HTML after reading the HTML itself. For this reason, the prediction advance is not enough to prefetch the objects and therefore there is no latency reduction. As a result of a wide analysis of the behaviour of the most commonly used algorithms, in this paper we present the DDG algorithm that distinguishes between container objects (HTML) and embedded objects to create a new prediction model according to the structure of the current web. Results show that, for the same amount of extra requests to the server, DDG always outperforms the existing algorithms by reducing the perceived latency between 15% and 150% more without increasing the computing complexity.

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Converging lines of evidence suggest that motor imagery (the mental simulation of a motor act within working memory) is associated with subliminal activation of the motor system. This observation has led to the hypothesis that cortical activation during motor imagery may affect the acquisition of specific motor skills and help the recovery of motor function. In this paper, we describe a clinical protocol in which we use interactive tools to stimulate motor imagery in hemiplegic stroke patients, thereby helping them to recover lost motor function. The protocol consists of an inpatient and an outpatient phase, combining physical and mental practice. In the inpatient phase, patients are trained in a laboratory setting, using a custom-made interactive workbench (VR Mirror). After discharge, patients use a portable device to guide mental and physical practice in a home setting. The proposed strategy is based on the hypotheses that: (a) combined physical and mental practice can make a cost-effective contribution to the rehabilitation of stroke patients, (b) effective mental practice is not possible without some form of support, from a therapist (as in our inpatient phase) or from technology (as in the outpatient phase), (c) the inclusion of an outpatient phase will allow the patient to practice more often than would otherwise be possible, therefore increasing the speed and/or effectiveness of learning, and (d) the use of interactive technology will reduce the patient's need for skilled support, therefore improving the cost-effectiveness of training.