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DeepMedic for Brain Tumor Segmentation

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Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries (BrainLes 2016)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 10154))

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Abstract

Accurate automatic algorithms for the segmentation of brain tumours have the potential of improving disease diagnosis, treatment planning, as well as enabling large-scale studies of the pathology. In this work we employ DeepMedic [1], a 3D CNN architecture previously presented for lesion segmentation, which we further improve by adding residual connections. We also present a series of experiments on the BRATS 2015 training database for evaluating the robustness of the network when less training data are available or less filters are used, aiming to shed some light on requirements for employing such a system. Our method was further benchmarked on the BRATS 2016 Challenge, where it achieved very good performance despite the simplicity of the pipeline.

K. Kamnitsas—Part of this work was carried on when KK was an intern at Microsoft.

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Notes

  1. 1.

    Code publicly available at: https://github.com/Kamnitsask/deepmedic.

  2. 2.

    Although these experiments were performed with the original version of the network, we expect the trends to continue after the extension with residual connections.

  3. 3.

    Code of the 3D CRF available at: https://github.com/Kamnitsask/dense3dCrf/.

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Acknowledgements

This work is supported by the EPSRC (grant No: EP/N023668/1) and partially funded under the 7th Framework Programme by the European Commission (TBIcare: http://www.tbicare.eu/; CENTER-TBI: https://www.center-tbi.eu/). Part of this work was carried on when KK was an intern at Microsoft Research Cambridge. KK is also supported by the President’s PhD Scholarship of Imperial College London. We gratefully acknowledge the support of NVIDIA Corporation with the donation of two Titan X GPUs for our research.

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Authors and Affiliations

  1. Biomedical Image Analysis Group, Imperial College London, London, UK

    Konstantinos Kamnitsas, Enzo Ferrante, Sarah Parisot, Christian Ledig, Daniel Rueckert & Ben Glocker

  2. Microsoft Research, Cambridge, UK

    Konstantinos Kamnitsas, Aditya V. Nori & Antonio Criminisi

Authors
  1. Konstantinos Kamnitsas
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  2. Enzo Ferrante
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  3. Sarah Parisot
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  4. Christian Ledig
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  5. Aditya V. Nori
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  6. Antonio Criminisi
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  7. Daniel Rueckert
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  8. Ben Glocker
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Corresponding author

Correspondence to Konstantinos Kamnitsas .

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Editors and Affiliations

  1. University of Zurich, Istituto Italiano di Tecnologia, Genoa, Italy

    Alessandro Crimi

  2. TU München, Computer Science, Munich, Germany

    Bjoern Menze

  3. Medical Informatics, University of Lübeck, Lübeck, Germany

    Oskar Maier

  4. Surgical Technology and Biomechanics, Universität Bern, Bern, Switzerland

    Mauricio Reyes

  5. Department of Medicine, University of Cambridge, Cambridge, United Kingdom

    Stefan Winzeck

  6. Institute of Medical Informatics, University of Lübeck, Lübeck, Germany

    Heinz Handels

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Kamnitsas, K. et al. (2016). DeepMedic for Brain Tumor Segmentation. In: Crimi, A., Menze, B., Maier, O., Reyes, M., Winzeck, S., Handels, H. (eds) Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries. BrainLes 2016. Lecture Notes in Computer Science(), vol 10154. Springer, Cham. https://doi.org/10.1007/978-3-319-55524-9_14

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  • DOI: https://doi.org/10.1007/978-3-319-55524-9_14

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