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Automatic reconstruction of vasculature

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Abstract

Two of the most difficult problems in Artificial Intelligence are processing visual scenes and processing natural languages. There has been a large amount of research in each of these fields but little on their integration. This is surprising given the potential importance of integrated systems, not only for understanding human cognition but also for the range of practical applications that will be enabled. We review previous work and provide an overview of our own work. We focus upon the medical application of reconstructing complicated cerebral blood vessel structures and associated pathologies from images and medical reports. This gives our work a clear and significant practical aim. We show how the ostensibly disparate technologies can be married using a single knowledge representation. Previous attempts at reconstruction have used images alone and no satisfactory solution exists. We believe that the synergy provided by integrating vision and natural language processing provides an information-rich environment that will enable progress toward an efficient and robust solution. Such an integration will have not only have important practical uses but also implications for Artificial Intelligence, Cognitive science, Philosophy, and Psychology.

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Authors and Affiliations

  1. Department of Computer Science, Regent Court, University of Sheffield, 211 Portobello Street, GB- S1 4DP, Sheffield, England, U.K., E.U.

    Paul Mc Kevitt

  2. Department of Computer Science, Victoria University Wellington, P.O. Box 600, Wellington, New Zealand

    Peter Hall

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  1. Paul Mc Kevitt
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  2. Peter Hall
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Mc Kevitt, P., Hall, P. Automatic reconstruction of vasculature. Artif Intell Rev 10, 235–252 (1996). https://doi.org/10.1007/BF00127681

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