The Essential Role of Open Data and Software for the Future of Ultrasound-Based Neuronavigation

With the recent developments in machine learning and modern graphics processing units (GPUs), there is a marked shift in the way intra-operative ultrasound (iUS) images can be processed and presented during surgery. Real-time processing of images to highlight important anatomical structures combined with in-situ display, has the potential to greatly facilitate the acquisition and interpretation of iUS images when guiding an operation. In order to take full advantage of the recent advances in machine learning, large amounts of high-quality annotated training data are necessary to develop and validate the algorithms. To ensure efficient collection of a sufficient number of patient images and external validity of the models, training data should be collected at several centers by different neurosurgeons, and stored in a standard format directly compatible with the most commonly used machine learning toolkits and libraries. In this paper, we argue that such effort to collect and organize large-scale multi-center datasets should be based on common open source software and databases. We first describe the development of existing open-source ultrasound based neuronavigation systems and how these systems have contributed to enhanced neurosurgical guidance over the last 15 years. We review the impact of the large number of projects worldwide that have benefited from the publicly available datasets “Brain Images of Tumors for Evaluation” (BITE) and “Retrospective evaluation of Cerebral Tumors” (RESECT) that include MR and US data from brain tumor cases. We also describe the need for continuous data collection and how this effort can be organized through the use of a well-adapted and user-friendly open-source software platform that integrates both continually improved guidance and automated data collection functionalities.publishedVersio

Word Knowledge and Word Usage

Word storage and processing define a multi-factorial domain of scientific inquiry whose thorough investigation goes well beyond the boundaries of traditional disciplinary taxonomies, to require synergic integration of a wide range of methods, techniques and empirical and experimental findings. The present book intends to approach a few central issues concerning the organization, structure and functioning of the Mental Lexicon, by asking domain experts to look at common, central topics from complementary standpoints, and discuss the advantages of developing converging perspectives. The book will explore the connections between computational and algorithmic models of the mental lexicon, word frequency distributions and information theoretical measures of word families, statistical correlations across psycho-linguistic and cognitive evidence, principles of machine learning and integrative brain models of word storage and processing. Main goal of the book will be to map out the landscape of future research in this area, to foster the development of interdisciplinary curricula and help single-domain specialists understand and address issues and questions as they are raised in other disciplines