RESUMEN
Automatic and reliable quantitative tools for MR brain image analysis are a very valuable resource for both clinical and research environments. In the past few years, this field has experienced many advances with successful techniques based on label fusion and more recently deep learning. However, few of them have been specifically designed to provide a dense anatomical labeling at the multiscale level and to deal with brain anatomical alterations such as white matter lesions (WML). In this work, we present a fully automatic pipeline (vol2Brain) for whole brain segmentation and analysis, which densely labels (N > 100) the brain while being robust to the presence of WML. This new pipeline is an evolution of our previous volBrain pipeline that extends significantly the number of regions that can be analyzed. Our proposed method is based on a fast and multiscale multi-atlas label fusion technology with systematic error correction able to provide accurate volumetric information in a few minutes. We have deployed our new pipeline within our platform volBrain (www.volbrain.upv.es), which has been already demonstrated to be an efficient and effective way to share our technology with the users worldwide.
RESUMEN
Smart subsystems like traffic, Smart Homes, the Smart Grid, outdoor lighting, etc. are built in many urban areas, each with a set of services that are offered to citizens. These subsystems are managed by self-contained embedded systems. However, coordination and cooperation between them are scarce. An integration of these systems which truly represents a "system of systems" could introduce more benefits, such as allowing the development of new applications and collective optimization. The integration should allow maximum reusability of available services provided by entities (e.g., sensors or Wireless Sensor Networks). Thus, it is of major importance to facilitate the discovery and registration of available services and subsystems in an integrated way. Therefore, an ontology-based and automatic system for subsystem and service registration and discovery is presented. Using this proposed system, heterogeneous subsystems and services could be registered and discovered in a dynamic manner with additional semantic annotations. In this way, users are able to build customized applications across different subsystems by using available services. The proposed system has been fully implemented and a case study is presented to show the usefulness of the proposed method.
RESUMEN
Context aware applications, which can adapt their behaviors to changing environments, are attracting more and more attention. To simplify the complexity of developing applications, context aware middleware, which introduces context awareness into the traditional middleware, is highlighted to provide a homogeneous interface involving generic context management solutions. This paper provides a survey of state-of-the-art context aware middleware architectures proposed during the period from 2009 through 2015. First, a preliminary background, such as the principles of context, context awareness, context modelling, and context reasoning, is provided for a comprehensive understanding of context aware middleware. On this basis, an overview of eleven carefully selected middleware architectures is presented and their main features explained. Then, thorough comparisons and analysis of the presented middleware architectures are performed based on technical parameters including architectural style, context abstraction, context reasoning, scalability, fault tolerance, interoperability, service discovery, storage, security & privacy, context awareness level, and cloud-based big data analytics. The analysis shows that there is actually no context aware middleware architecture that complies with all requirements. Finally, challenges are pointed out as open issues for future work.
RESUMEN
BACKGROUND: Superficial bladder cancer has been the subject of numerous studies for many years, but the evolution of the disease still remains not well understood. After the tumor has been surgically removed, it may reappear at a similar level of malignancy or progress to a higher level. The process may be reasonably modeled by means of a Markov process. However, in order to more completely model the evolution of the disease, this approach is insufficient. The semi-Markov framework allows a more realistic approach, but calculations become frequently intractable. In this context, flowgraph models provide an efficient approach to successfully manage the evolution of superficial bladder carcinoma. Our aim is to test this methodology in this particular case. RESULTS: We have built a successful model for a simple but representative case. CONCLUSION: The flowgraph approach is suitable for modeling of superficial bladder cancer.