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Domain-Agnostic Segmentation of Thalamic Nuclei from Joint Structural and Diffusion MRI

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Medical Image Computing and Computer Assisted Intervention – MICCAI 2023 (MICCAI 2023)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 14227))

Abstract

The human thalamus is a subcortical brain structure that comprises dozens of nuclei with different function and connectivity, which are affected differently by disease. For this reason, there is growing interest in studying the thalamic nuclei in vivo with MRI. Tools are available to segment the thalamus from 1 mm T1 scans, but the image contrast is too faint to produce reliable segmentations. Some tools have attempted to refine these boundaries using diffusion MRI information, but do not generalise well across diffusion MRI acquisitions. Here we present the first CNN that can segment thalamic nuclei from T1 and diffusion data of any resolution without retraining or fine tuning. Our method builds on our histological atlas of the thalamic nuclei and silver standard segmentations on high-quality diffusion data obtained with our recent Bayesian adaptive segmentation tool. We combine these with an approximate degradation model for fast domain randomisation during training. Our CNN produces a segmentation at 0.7 mm isotropic resolution, irrespective of the resolution of the input. Moreover, it uses a parsimonious model of the diffusion signal (fractional anisotropy and principal eigenvector) that is compatible with virtually any set of directions and b-values, including huge amounts of legacy data. We show results of our proposed method on three heterogeneous datasets acquired on dozens of different scanners. The method is publicly available at freesurfer.net/fswiki/ThalamicNucleiDTI.

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Acknowledgments

Work primarily funded by ARUK (IRG2019A003). Additional support by the NIH (RF1MH123195, R01AG070988, P41EB015902, R01EB021265, R56MH121426, R01NS112161), EPSRC (EP/R006032/1), Wellcome Trust (221915/Z/20/Z), Alzheimer’s Society (AS-JF-19a-004-517), Brain Research UK, Wolfson; UK NIHR (BRC149/NS/MH), UK MRC (MR/M008525/1), Marie Curie (765148), ERC (677697), and Miriam Marks Brain Research.

Author information

Authors and Affiliations

  1. Centre for Medical Image Computing, UCL, London, UK

    Henry F. J. Tregidgo, Sonja Soskic, Juri Althonayan, Benjamin Billot, Daniel C. Alexander & Juan Eugenio Iglesias

  2. Neurostatistics Research Laboratory, MIT, Cambridge, USA

    Mark D. Olchanyi

  3. Neurotechnology and Neurorecovery, MGH and Harvard Medical School, Boston, USA

    Mark D. Olchanyi

  4. Computer Science and Artificial Intelligence Laboratory, MIT, Cambridge, USA

    Benjamin Billot, Polina Golland & Juan Eugenio Iglesias

  5. Martinos Center for Biomedical Imaging, MGH and Harvard Medical School, Boston, USA

    Chiara Maffei, Anastasia Yendiki & Juan Eugenio Iglesias

  6. Centre for Cognitive and Clinical Neuroscience, Brunel University, London, UK

    Martina Bocchetta

  7. Dementia Research Centre, UCL, London, UK

    Martina Bocchetta & Jonathan D. Rohrer

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  1. Henry F. J. Tregidgo
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Corresponding author

Correspondence to Henry F. J. Tregidgo .

Editor information

Editors and Affiliations

  1. Icahn School of Medicine, Mount Sinai, NYC, NY, USA, Tel Aviv University, Tel Aviv, Israel

    Hayit Greenspan

  2. Emory University, Atlanta, GA, USA

    Anant Madabhushi

  3. Queen’s University, Kingston, ON, Canada

    Parvin Mousavi

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

    Septimiu Salcudean

  5. Yale University, New Haven, CT, USA

    James Duncan

  6. IBM Research, San Jose, CA, USA

    Tanveer Syeda-Mahmood

  7. Johns Hopkins University, Baltimore, MD, USA

    Russell Taylor

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Tregidgo, H.F.J. et al. (2023). Domain-Agnostic Segmentation of Thalamic Nuclei from Joint Structural and Diffusion MRI. In: Greenspan, H., et al. Medical Image Computing and Computer Assisted Intervention – MICCAI 2023. MICCAI 2023. Lecture Notes in Computer Science, vol 14227. Springer, Cham. https://doi.org/10.1007/978-3-031-43993-3_24

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  • DOI: https://doi.org/10.1007/978-3-031-43993-3_24

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