John S H Baxter1, Quoc Anh Bui2, Ehouarn Maguet2, Stéphane Croci3, Antoine Delmas3, Jean-Pascal Lefaucheur4,5, Luc Bredoux3, Pierre Jannin2. 1. Laboratoire Traitement du Signal et de l'Image (LTSI - INSERM UMR 1099), Université de Rennes 1, Rennes, France. jbaxter@univ-rennes1.fr. 2. Laboratoire Traitement du Signal et de l'Image (LTSI - INSERM UMR 1099), Université de Rennes 1, Rennes, France. 3. SYNEIKA, Rennes, France. 4. ENT Team, EA4391, Faculty of Medicine, Paris Est Créteil University, Créteil, France. 5. Clinical Neurophysiology Unit, Department of Physiology, Henri Mondor Hospital, Assistance Publique - Hôpitaux de Paris, Créteil, France.
Abstract
PURPOSE: 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. METHODS: 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. RESULTS: Preliminary experiments have found the accuracy of this network to be [Formula: see text] mm, compared to [Formula: see text] mm for deformable registration and [Formula: see text] 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. CONCLUSIONS: 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.
PURPOSE: 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. METHODS: 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. RESULTS: Preliminary experiments have found the accuracy of this network to be [Formula: see text] mm, compared to [Formula: see text] mm for deformable registration and [Formula: see text] 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. CONCLUSIONS: 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.
Entities:
Keywords:
Convolutional neural networks; Deep learning; Transcranial magnetic stimulation