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Robust Image Registration with Absent Correspondences in Pre-operative and Follow-Up Brain MRI Scans of Diffuse Glioma Patients

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Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries (BrainLes 2022)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13769))

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Abstract

Registration of pre-operative and follow-up brain MRI scans is challenging due to the large variation of tissue appearance and missing correspondences in tumour recurrence regions caused by tumour mass effect. Although recent deep learning-based deformable registration methods have achieved remarkable success in various medical applications, most of them are not capable of registering images with pathologies. In this paper, we propose a 3-step registration pipeline for pre-operative and follow-up brain MRI scans that consists of 1) a multi-level affine registration, 2) a conditional deep Laplacian pyramid image registration network (cLapIRN) with forward-backward consistency constraint, and 3) a non-linear instance optimization method. We apply the method to the Brain Tumor Sequence Registration (BraTS-Reg) Challenge. Our method achieves accurate and robust registration of brain MRI scans with pathologies, which achieves a median absolute error of 1.64 mm and 88% of successful registration rate in the validation set of BraTS-Reg challenge. Our method ranks 1\(^\text {st}\) place in the 2022 MICCAI BraTS-Reg challenge.

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Authors and Affiliations

  1. The Hong Kong University of Science and Technology, Hong Kong, China

    Tony C. W. Mok & Albert C. S. Chung

Authors
  1. Tony C. W. Mok
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  2. Albert C. S. Chung
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Corresponding author

Correspondence to Tony C. W. Mok .

Editor information

Editors and Affiliations

  1. University of Pennsylvania, Philadelphia, PA, USA

    Spyridon Bakas

  2. Sano, Center for Computational Personalised Medicine, Kraków, Poland

    Alessandro Crimi

  3. University of Pennsylvania, Philadelphia, PA, USA

    Ujjwal Baid

  4. Sano, Center for Computational Personalised Medicine, Kraków, Poland

    Sylwia Malec

  5. Sano, Center for Computational Personalised Medicine, Kraków, Poland

    Monika Pytlarz

  6. University of Pennsylvania, Philadelphia, PA, USA

    Bhakti Baheti

  7. German Cancer Research Center, Heidelberg, Germany

    Maximilian Zenk

  8. Harvard Medical School, Boston, MA, USA

    Reuben Dorent

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Mok, T.C.W., Chung, A.C.S. (2023). Robust Image Registration with Absent Correspondences in Pre-operative and Follow-Up Brain MRI Scans of Diffuse Glioma Patients. In: Bakas, S., et al. Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries. BrainLes 2022. Lecture Notes in Computer Science, vol 13769. Springer, Cham. https://doi.org/10.1007/978-3-031-33842-7_20

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  • DOI: https://doi.org/10.1007/978-3-031-33842-7_20

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  • Online ISBN: 978-3-031-33842-7

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