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International Conference on Medical Image Computing and Computer-Assisted Intervention

MICCAI 2020: Medical Image Computing and Computer Assisted Intervention – MICCAI 2020 pp 593–602Cite as

Differentiable Deconvolution for Improved Stroke Perfusion Analysis

Part of the Lecture Notes in Computer Science book series (LNIP,volume 12267)

Abstract

Perfusion imaging is the current gold standard for acute ischemic stroke analysis. It allows quantification of the salvageable and non-salvageable tissue regions (penumbra and core areas respectively). In clinical settings, the singular value decomposition (SVD) deconvolution is one of the most accepted and used approaches for generating interpretable and physically meaningful maps. Though this method has been widely validated in experimental and clinical settings, it might produce suboptimal results because the chosen inputs to the model cannot guarantee optimal performance. For the most critical input, the arterial input function (AIF), it is still controversial how and where it should be chosen even though the method is very sensitive to this input. In this work we propose an AIF selection approach that is optimized for maximal core lesion segmentation performance. The AIF is regressed by a neural network optimized through a differentiable SVD deconvolution, aiming to maximize core lesion segmentation agreement with ground truth data. To our knowledge, this is the first work exploiting a differentiable deconvolution model with neural networks. We show that our approach is able to generate AIFs without any manual annotation, and hence avoiding manual rater’s influences. The method achieves manual expert performance in the ISLES18 dataset. We conclude that the methodology opens new possibilities for improving perfusion imaging quantification with deep neural networks.

Keywords

  • Perfusion imaging
  • SVD deconvolution
  • Deep learning

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Acknowledgements

This project received funding from the European Union’s Horizon 2020 research and innovation program under the Marie Sklodowska-Curie grant agreement TRABIT No 765148. EDLR, DR and DMS are employees of icometrix. DR is supported by an innovation mandate of Flanders Innovation & Entrepreneurship (VLAIO).

Author information

Authors and Affiliations

  1. icometrix, Leuven, Belgium

    Ezequiel de la Rosa, David Robben & Diana M. Sima

  2. Department of Computer Science, Technical University of Munich, Munich, Germany

    Ezequiel de la Rosa & Bjoern Menze

  3. Medical Imaging Research Center (MIRC), KU Leuven, Leuven, Belgium

    David Robben

  4. Department of Electrical Engineering, ESAT/PSI, KU Leuven, Leuven, Belgium

    David Robben

  5. Neuroradiology, School of Medicine, Technical University of Munich, Munich, Germany

    Jan S. Kirschke

Authors
  1. Ezequiel de la Rosa
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  2. David Robben
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  3. Diana M. Sima
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  4. Jan S. Kirschke
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  5. Bjoern Menze
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Corresponding author

Correspondence to Ezequiel de la Rosa .

Editor information

Editors and Affiliations

  1. University of Toronto, Toronto, ON, Canada

    Prof. Anne L. Martel

  2. The University of British Columbia, Vancouver, BC, Canada

    Purang Abolmaesumi

  3. University College London, London, UK

    Danail Stoyanov

  4. École Centrale de Nantes, Nantes, France

    Diana Mateus

  5. EURECOM, Biot, France

    Maria A. Zuluaga

  6. Chinese Academy of Sciences, Beijing, China

    S. Kevin Zhou

  7. Sorbonne University, Paris, France

    Daniel Racoceanu

  8. The Hebrew University of Jerusalem, Jerusalem, Israel

    Prof. Leo Joskowicz

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de la Rosa, E., Robben, D., Sima, D.M., Kirschke, J.S., Menze, B. (2020). Differentiable Deconvolution for Improved Stroke Perfusion Analysis. In: , et al. Medical Image Computing and Computer Assisted Intervention – MICCAI 2020. MICCAI 2020. Lecture Notes in Computer Science(), vol 12267. Springer, Cham. https://doi.org/10.1007/978-3-030-59728-3_58

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  • DOI: https://doi.org/10.1007/978-3-030-59728-3_58

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