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International MICCAI Brainlesion Workshop

BrainLes 2020: Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries pp 168–180Cite as

Bayesian Skip Net: Building on Prior Information for the Prediction and Segmentation of Stroke Lesions

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Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 12658))

Abstract

Perfusion CT is widely used in acute ischemic stroke to determine eligibility for acute treatment, by defining an ischemic core and penumbra. In this work, we propose a novel way of building on prior information for the automatic prediction and segmentation of stroke lesions. To this end, we reformulate the task to identify differences from a prior segmentation by extending a three-dimensional Attention Gated Unet with a skip connection allowing only an unchanged prior to bypass most of the network. We show that this technique improves results obtained by a baseline Attention Gated Unet on both the Geneva Stroke Dataset and the ISLES 2018 dataset.

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Author information

Authors and Affiliations

  1. Stroke Research Group, Department of Clinical Neurosciences, University Hospital and Faculty of Medicine, Geneva, Switzerland

    Julian Klug, Elisabeth Dirren & Emmanuel Carrera

  2. Medical Image Processing Laboratory, Institute of Bioengineering, Ecole Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland

    Julian Klug, Maria Giulia Preti & Dimitri Van De Ville

  3. Massachusetts Institute of Technology, Cambridge, USA

    Guillaume Leclerc

Authors
  1. Julian Klug
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  2. Guillaume Leclerc
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  3. Elisabeth Dirren
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  4. Maria Giulia Preti
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  5. Dimitri Van De Ville
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  6. Emmanuel Carrera
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Corresponding author

Correspondence to Julian Klug .

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Editors and Affiliations

  1. University of Zurich, Zurich, Switzerland

    Alessandro Crimi

  2. University of Pennsylvania, Philadelphia, PA, USA

    Spyridon Bakas

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Klug, J., Leclerc, G., Dirren, E., Preti, M.G., Van De Ville, D., Carrera, E. (2021). Bayesian Skip Net: Building on Prior Information for the Prediction and Segmentation of Stroke Lesions. In: Crimi, A., Bakas, S. (eds) Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries. BrainLes 2020. Lecture Notes in Computer Science(), vol 12658. Springer, Cham. https://doi.org/10.1007/978-3-030-72084-1_16

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  • DOI: https://doi.org/10.1007/978-3-030-72084-1_16

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  • Print ISBN: 978-3-030-72083-4

  • Online ISBN: 978-3-030-72084-1

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