Automatic cortical target point localisation in MRI for transcranial magnetic stimulation via a multi-resolution convolutional neural network



Transcranial magnetic stimulation (TMS) is a growing therapy for a variety of psychiatric and neurological disorders that arise from or are modulated by cortical regions of the brain represented by singular 3D target points. These target points are often determined manually with assistance from a pre-operative T1-weighted MRI, although there is growing interest in automatic target point localisation using an atlas. However, both approaches can be time-consuming which has an effect on the clinical workflow, and the latter does not take into account patient variability such as the varying number of cortical gyri where these targets are located.


This paper proposes a multi-resolution convolutional neural network for point localisation in MR images for a priori defined points in increasingly finely resolved versions of the input image. This approach is both fast and highly memory efficient, allowing it to run in high-throughput centres, and has the capability of distinguishing between patients with high levels of anatomical variability.


Preliminary experiments have found the accuracy of this network to be \(7.26\pm 5.30\) mm, compared to \(9.39\pm 4.63\) mm for deformable registration and \(6.94\pm 5.10\) mm for a human expert. For most treatment points, the human expert and proposed CNN statistically significantly outperform registration, but neither statistically significantly outperforms the other, suggesting that the proposed network has human-level performance.


The human-level performance of this network indicates that it can improve TMS planning by automatically localising target points in seconds, avoiding more time-consuming registration or manual point localisation processes. This is particularly beneficial for out-of-hospital centres with limited computational resources where TMS is increasingly being administered.

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Data are not available for this study.


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John S. H. Baxter is supported by the Institut des Neurosciences Cliniques de Rennes (INCR) and the Natural Sciences and Engineering Research Council of Canada (NSERC) through the Post-Doctoral Fellowship (PDF) program. Quoc Anh Bui is funded through a financial support from Région Bretagne. Ehouarn Maguet is also supported INCR. The authors would like to thank X. Morandi, C. Nauczyciel, B. Le Goff, and J.-P. N’Guyen for the annotation of the non-motor treatment points. The authors would also like to thank J.-P. N’Guyen and H. Hodaj for their assistance in annotating the chronic pain treatment points along with J.-P. Lefaucheur.


No funding was received to assist in the preparation of this manuscript.

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Correspondence to John S. H. Baxter.

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S. Croci, A. Delmas and L. Bredoux are employees of SYNEIKA. The remaining authors have no financial or non-financial conflicts of interest.

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All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards.

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Baxter, J.S.H., Bui, Q.A., Maguet, E. et al. Automatic cortical target point localisation in MRI for transcranial magnetic stimulation via a multi-resolution convolutional neural network. Int J CARS 16, 1077–1087 (2021).

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  • Transcranial magnetic stimulation
  • Deep learning
  • Convolutional neural networks