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International Symposium on Distributed Computing and Artificial Intelligence

DCAI 2018: Distributed Computing and Artificial Intelligence, 15th International Conference pp 324–331Cite as

Combining Image and Non-image Clinical Data: An Infrastructure that Allows Machine Learning Studies in a Hospital Environment

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Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 800))

Abstract

Over the past years Machine Learning and Deep Learning techniques are showing their huge potential in medical research. However, this research is mainly done by using public or private datasets that were created for study purposes. Despite ensuring reproducibility, these datasets need to be constantly updated.

In this paper we present an infrastructure that transfers, processes and stores medical image and non-image data in an organized and secure workflow. This infrastructure concept has been tested at a university hospital. XNAT, an extensible open-source imaging informatics software platform was extended to store the non-image data and later feed the Machine Learning models. The resulting infrastructure allowed an easy implementation of a Deep Learning approach for brain tumor segmentation with potential for other medical image research scenarios.

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Acknowledgements

This work was supported by University Hospital Cologne, Department of Radiology and Philips Research Aachen. It was also supported by COMPETE: POCI-01-0145-FEDER-007043 and FCT – Fundação para a Ciência e Tecnologia within the Project Scope: UID/CEC/00319/2013.

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Authors and Affiliations

  1. University Hospital Cologne, Cologne, Germany

    Raphael Espanha, Frank Thiele, Georgy Shakirin, Jens Roggenfelder, Sascha Zeiter, Pantelis Stavrinou & Michael Perkuhn

  2. Philips Research Laboratories Aachen, Aachen, Germany

    Raphael Espanha, Frank Thiele, Georgy Shakirin & Michael Perkuhn

  3. Department of Informatics, University of Minho, Braga, Portugal

    Raphael Espanha

  4. Centro Algoritmi, University of Minho, Braga, Portugal

    Victor Alves

Authors
  1. Raphael Espanha
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  2. Frank Thiele
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  3. Georgy Shakirin
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  4. Jens Roggenfelder
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  5. Sascha Zeiter
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  6. Pantelis Stavrinou
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  7. Victor Alves
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  8. Michael Perkuhn
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Corresponding author

Correspondence to Victor Alves .

Editor information

Editors and Affiliations

  1. BISITE Digital Innovation Hub. Edificio Multimedia I+D+i, University of Salamanca, Salamanca, Spain

    Fernando De La Prieta

  2. Faculty of Engineering, Department of Electronics, Information and Communication, Osaka Institute of Technology, Osaka, Osaka, Japan

    Sigeru Omatu

  3. Department of Computing Systems, School of Industrial Engineers at Albacete, University of Castilla-La Mancha, Albacete, Spain

    Antonio Fernández-Caballero

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Espanha, R. et al. (2019). Combining Image and Non-image Clinical Data: An Infrastructure that Allows Machine Learning Studies in a Hospital Environment. In: De La Prieta, F., Omatu, S., Fernández-Caballero, A. (eds) Distributed Computing and Artificial Intelligence, 15th International Conference. DCAI 2018. Advances in Intelligent Systems and Computing, vol 800. Springer, Cham. https://doi.org/10.1007/978-3-319-94649-8_39

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