3 resultados para Augmented-Reality
em Université de Lausanne, Switzerland
Resumo:
The mouse has emerged as an animal model for many diseases. At IRO, we have used this animal to understand the development of many eye diseases and treatment of some of them. Precise evaluation of vision is a prerequisite for both these approaches. In this unit we describe three ways to measure vision: testing the optokinetic response, and evaluating the fundus by direct observation and by fluorescent angiography.
Resumo:
Introduction. Preoperative malnutrition is a major risk factor for increased postoperative morbidity and mortality. Definition and diagnosis of malnutrition and its treatment is still subject for controversy. Furthermore, practical implementation of nutrition-related guidelines is unknown. Methods. A review of the available literature and of current guidelines on perioperative nutrition was conducted. We focused on nutritional screening and perioperative nutrition in patients undergoing digestive surgery, and we assessed translation of recent guidelines in clinical practice. Results and Conclusions. Malnutrition is a well-recognized risk factor for poor postoperative outcome. The prevalence of malnutrition depends largely on its definition; about 40% of patients undergoing major surgery fulfil current diagnostic criteria of being at nutritional risk. The Nutritional Risk Score is a pragmatic and validated tool to identify patients who should benefit from nutritional support. Adequate nutritional intervention entails reduced (infectious) complications, hospital stay, and costs. Preoperative oral supplementation of a minimum of five days is preferable; depending on the patient and the type of surgery, immune-enhancing formulas are recommended. However, surgeons' compliance with evidence-based guidelines remains poor and efforts are necessary to implement routine nutritional screening and nutritional support.
Resumo:
In this paper, we present an efficient numerical scheme for the recently introduced geodesic active fields (GAF) framework for geometric image registration. This framework considers the registration task as a weighted minimal surface problem. Hence, the data-term and the regularization-term are combined through multiplication in a single, parametrization invariant and geometric cost functional. The multiplicative coupling provides an intrinsic, spatially varying and data-dependent tuning of the regularization strength, and the parametrization invariance allows working with images of nonflat geometry, generally defined on any smoothly parametrizable manifold. The resulting energy-minimizing flow, however, has poor numerical properties. Here, we provide an efficient numerical scheme that uses a splitting approach; data and regularity terms are optimized over two distinct deformation fields that are constrained to be equal via an augmented Lagrangian approach. Our approach is more flexible than standard Gaussian regularization, since one can interpolate freely between isotropic Gaussian and anisotropic TV-like smoothing. In this paper, we compare the geodesic active fields method with the popular Demons method and three more recent state-of-the-art algorithms: NL-optical flow, MRF image registration, and landmark-enhanced large displacement optical flow. Thus, we can show the advantages of the proposed FastGAF method. It compares favorably against Demons, both in terms of registration speed and quality. Over the range of example applications, it also consistently produces results not far from more dedicated state-of-the-art methods, illustrating the flexibility of the proposed framework.