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Bayesian Filtered Generation of Post-surgical Brain Connectomes on Tumor Patients

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Imaging Systems for GI Endoscopy, and Graphs in Biomedical Image Analysis (ISGIE 2022, GRAIL 2022)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13754))

Abstract

Graph representation learning methods have recently been applied to predict how brain functional and structural networks will evolve in time. However, to obtain minimally coherent predictions, these methods require large datasets that are rarely available in sensitive settings such as brain tumors. Because of this, the problem of plasticity reorganization after tumor resection has been largely neglected in the machine learning community despite having an enormous potential for surgical planning. We present a machine learning model able to predict brain graphs following brain surgery, which can provide valuable information to surgeons planning better surgery. We rely on the idea that surgical outcomes share network similarities with healthy subjects and combine them in a Bayesian approach. We show how our method significantly outperforms simpler models even when taking advantage of the same prior. Furthermore, generated brain graphs share topological features with the real brain graphs. Overall, we present the problem of plasticity reorganization after brain surgery in a normative manner while still achieving competitive results.

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Acknowledgement

This research was supported by European Union’s Horizon 2020 program [Sano No 857533] and by the Foundation for Polish Science [Sano project].

Author information

Authors and Affiliations

  1. Sano – Centre for Personalised Computational Medicine, Czarnowiejska 36 building C5, 30-054, Kraków, Poland

    Joan Falcó-Roget & Alessandro Crimi

Authors
  1. Joan Falcó-Roget
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  2. Alessandro Crimi
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Corresponding author

Correspondence to Joan Falcó-Roget .

Editor information

Editors and Affiliations

  1. University of Dundee, Dundee, UK

    Luigi Manfredi

  2. Nvidia, Munich, Germany

    Seyed-Ahmad Ahmadi

  3. University of Oxford, Oxford, UK

    Michael Bronstein

  4. Harvard University, Boston, USA

    Anees Kazi

  5. National University of Singapore, Singapore, Singapore

    Davide Lomanto

  6. University of Dundee, Dundee, UK

    Alwyn Mathew

  7. University of Dundee, Dundee, UK

    Ludovic Magerand

  8. Technical University of Munich, Munich, Germany

    Kamilia Mullakaeva

  9. University of Oxford, Oxford, UK

    Bartlomiej Papiez

  10. Johns Hopkins University, Baltimore, USA

    Russell H. Taylor

  11. University of Dundee, Dundee, UK

    Emanuele Trucco

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Falcó-Roget, J., Crimi, A. (2022). Bayesian Filtered Generation of Post-surgical Brain Connectomes on Tumor Patients. In: Manfredi, L., et al. Imaging Systems for GI Endoscopy, and Graphs in Biomedical Image Analysis. ISGIE GRAIL 2022 2022. Lecture Notes in Computer Science, vol 13754. Springer, Cham. https://doi.org/10.1007/978-3-031-21083-9_8

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  • DOI: https://doi.org/10.1007/978-3-031-21083-9_8

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-21082-2

  • Online ISBN: 978-3-031-21083-9

  • eBook Packages: Computer ScienceComputer Science (R0)

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