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

2019
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Authors: Raphael Espanha; Frank Thiele; Georgy Shakirin; Jens Roggenfelder; Sascha Zeiter; Pantelis Stavrinou; Victor Alves; Michael Perkuhn
Published in: Distributed Computing and Artificial Intelligence, 15th International Conference
Publisher: Springer International Publishing

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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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Title: Combining Image and Non-image Clinical Data: An Infrastructure that Allows Machine Learning Studies in a Hospital Environment
Authors: Raphael Espanha
Frank Thiele
Georgy Shakirin
Jens Roggenfelder
Sascha Zeiter
Pantelis Stavrinou
Victor Alves
Michael Perkuhn
Publisher: Springer International Publishing
Book: Distributed Computing and Artificial Intelligence, 15th International Conference
Print ISBN: 978-3-319-94648-1

Electronic ISBN: 978-3-319-94649-8

Copyright Year: 2019
DOI: https://doi.org/10.1007/978-3-319-94649-8_39

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