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An object-oriented library for systematic training and comparison of classifiers for computer-assisted tumor diagnosis from MRSI measurements

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Computer Science - Research and Development

Abstract

We present an object-oriented library for the systematic training, testing and benchmarking of classification algorithms for computer-assisted diagnosis tasks, with a focus on tumor probability estimation from magnetic resonance spectroscopy imaging (MRSI) measurements. In connection with a graphical user interface for data annotation, it allows clinical end users to flexibly adapt these classifiers towards changed classification tasks, to benchmark various classifiers and preprocessing steps and to perform quality control of the results. This poses an advantage over previous classification software solutions, which required expert knowledge in pattern recognition techniques in order to adapt them to changes in the data acquisition protocols. This software will constitute a major part of the MRSI analysis functionality of RONDO, an integrated software platform for cancer diagnosis and therapy planning which is under current development.

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Authors and Affiliations

  1. Heidelberg Collaboratory for Image Processing, University of Heidelberg, Speyerer Straße 6, 69115, Heidelberg, Germany

    Frederik O. Kaster & Fred A. Hamprecht

  2. German Cancer Research Center, Im Neuenheimer Feld 280, 69120, Heidelberg, Germany

    Frederik O. Kaster & Oliver Nix

  3. Fraunhofer MeVis Institute for Medical Image Computing, Universitätsallee 29, 28359, Bremen, Germany

    Bernd Merkel

Authors
  1. Frederik O. Kaster
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  2. Bernd Merkel
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  3. Oliver Nix
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  4. Fred A. Hamprecht
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Corresponding author

Correspondence to Frederik O. Kaster.

Additional information

This research was supported by the Helmholtz International Graduate School for Cancer Research and by the German Federal Ministry for Education and Research within the DOT-MOBI project (grant no. 01IB08002).

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Kaster, F.O., Merkel, B., Nix, O. et al. An object-oriented library for systematic training and comparison of classifiers for computer-assisted tumor diagnosis from MRSI measurements. Comput Sci Res Dev 26, 65–85 (2011). https://doi.org/10.1007/s00450-010-0143-z

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  • DOI: https://doi.org/10.1007/s00450-010-0143-z

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