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Annual Conference on Medical Image Understanding and Analysis

MIUA 2017: Medical Image Understanding and Analysis pp 448–458Cite as

A Deep Learning Pipeline to Delineate Proliferative Areas of Intracranial Tumors in Digital Slides

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Part of the book series: Communications in Computer and Information Science ((CCIS,volume 723))

Abstract

Separating tumor cells from other tissues such as meninges and blood is one of the vital steps towards automated quantification of the proliferative index in digital slides of brain tumors. In this paper, we present a deep learning based pipeline to delineate areas of tumor in meningioma and oligodendroglioma specimens stained with Ki-67 marker. A pre-trained convolutional neural network (CNN) was fine-tuned with 7057 image tiles to classify whole slide images (n = 15) in a tile-by-tile mode. The performance of the model was evaluated on slides manually annotated by the pathologist. The CNN model detected tumor areas with 89.4% accuracy. Areas with blood and meninges were respectively classified with 98.2% and 89.8% accuracy. The overall classification accuracy was 88.7%, and the Cohen’s kappa coefficient reached 0.748, indicating a very good concordance with the manual ground truth. Our pipeline can process digital slides at full resolution, and has the potential to objectively pre-process slides for proliferative index quantification.

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Acknowledgment

This study was supported by the National Centre for Research and Development, Poland (grant PBS2/A9/21/2013).

Author information

Authors and Affiliations

  1. Faculty of Electrical Engineering, Warsaw University of Technology, Warsaw, Poland

    Zaneta Swiderska-Chadaj & Tomasz Markiewicz

  2. Department of Pathomorphology, Military Institute of Medicine, Warsaw, Poland

    Tomasz Markiewicz, Bartlomiej Grala & Malgorzata Lorent

  3. Bioimage Informatics Laboratory, Department of Surgery, Cedars-Sinai Medical Center, Los Angeles, CA, USA

    Arkadiusz Gertych

Authors
  1. Zaneta Swiderska-Chadaj
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  2. Tomasz Markiewicz
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  3. Bartlomiej Grala
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  4. Malgorzata Lorent
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  5. Arkadiusz Gertych
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Corresponding author

Correspondence to Zaneta Swiderska-Chadaj .

Editor information

Editors and Affiliations

  1. Department of Neuroimaging Sciences, University of Edinburgh, Edinburgh, United Kingdom

    María Valdés Hernández

  2. University of León, León, Spain

    Víctor González-Castro

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Swiderska-Chadaj, Z., Markiewicz, T., Grala, B., Lorent, M., Gertych, A. (2017). A Deep Learning Pipeline to Delineate Proliferative Areas of Intracranial Tumors in Digital Slides. In: Valdés Hernández, M., González-Castro, V. (eds) Medical Image Understanding and Analysis. MIUA 2017. Communications in Computer and Information Science, vol 723. Springer, Cham. https://doi.org/10.1007/978-3-319-60964-5_39

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  • DOI: https://doi.org/10.1007/978-3-319-60964-5_39

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-60963-8

  • Online ISBN: 978-3-319-60964-5

  • eBook Packages: Computer ScienceComputer Science (R0)

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