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A Fast Semi-Automatic Segmentation Tool for Processing Brain Tumor Images

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Towards Integrative Machine Learning and Knowledge Extraction

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 10344))

Abstract

Segmentation, the process of delineating boundaries and features within images, is a vital part of both the clinical assessment and the computational analysis of brain cancers. Here, we provide an open-source algorithm (MITKats), built on the Medical Imaging Interaction Toolkit, to provide user-friendly and expedient tools for semi-automatic segmentation. To evaluate its performance against competing algorithms, we applied MITKats to MRIs of 38 high-grade glioma cases from publicly available benchmarks. The similarity of the segmentations to expert-delineated ground truths approached the discrepancies among different manual raters, the theoretically maximal precision. The average time spent on each segmentation was 5 min, making MITKats between 4 and 11 times faster than competing semi-automatic algorithms, while retaining similar accuracy. We conclude with remarks on the utility of segmentation for medical data analysis as well as its further challenges.

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Acknowledgments

We would like to thank Anthea Monod and the rest of the Rabadan lab for their helpful comments and feedback.

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Authors and Affiliations

  1. Department of Systems Biology, Columbia University Medical Center, 1130 St. Nicholas Avenue, New York, NY, 10032, USA

    Andrew X. Chen & Raúl Rabadán

  2. Department of Biomedical Informatics, Columbia University Medical Center, 1130 St. Nicholas Avenue, New York, NY, 10032, USA

    Raúl Rabadán

Authors
  1. Andrew X. Chen
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  2. Raúl Rabadán
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Corresponding author

Correspondence to Raúl Rabadán .

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Editors and Affiliations

  1. Medical University Graz, Graz, Austria

    Andreas Holzinger

  2. University of Alberta, Edmonton, Alberta, Canada

    Randy Goebel

  3. Bologna University, Bologna, Italy

    Massimo Ferri

  4. Coventry University, Coventry, United Kingdom

    Vasile Palade

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Chen, A.X., Rabadán, R. (2017). A Fast Semi-Automatic Segmentation Tool for Processing Brain Tumor Images. In: Holzinger, A., Goebel, R., Ferri, M., Palade, V. (eds) Towards Integrative Machine Learning and Knowledge Extraction. Lecture Notes in Computer Science(), vol 10344. Springer, Cham. https://doi.org/10.1007/978-3-319-69775-8_10

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  • DOI: https://doi.org/10.1007/978-3-319-69775-8_10

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-69774-1

  • Online ISBN: 978-3-319-69775-8

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