Laboratoire Traitement du Signal et de l'Image (LTSI - INSERM UMR 1099), Université de Rennes 1, Rennes, France.
SYNEIKA, Rennes, France.
Int J Comput Assist Radiol Surg. 2021 Jul;16(7):1077-1087. doi: 10.1007/s11548-021-02386-1. Epub 2021 Jun 5.
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 [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.
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.
经颅磁刺激（TMS）是一种不断发展的治疗方法，适用于各种源于大脑皮质区域或受其调节的精神和神经疾病。这些靶点通常通过术前 T1 加权 MRI 手动确定，尽管人们越来越感兴趣地使用图谱自动定位靶点。然而，这两种方法都很耗时，会影响临床工作流程，而后者则没有考虑到患者的个体差异，例如这些靶点所在的皮质脑回数量不同。
本文提出了一种用于在 MR 图像中定位先验定义的点的多分辨率卷积神经网络，该网络针对输入图像的逐渐细化版本进行点定位。这种方法既快速又具有高度的内存效率，使其能够在高通量中心运行，并且能够区分具有高度解剖结构变异的患者。
初步实验发现，该网络的准确性为[公式：见文本]mm，与变形配准的[公式：见文本]mm 和人类专家的[公式：见文本]mm 相比。对于大多数治疗靶点，人类专家和提出的 CNN 在统计学上明显优于配准，但两者都没有在统计学上显著优于对方，这表明所提出的网络具有人类水平的性能。
该网络的人类水平性能表明，它可以通过在几秒钟内自动定位靶点来改善 TMS 规划，避免更耗时的配准或手动定位过程。对于计算资源有限的院外中心，TMS 治疗越来越普及，这一点尤其有益。
