Learning Expected Appearances for Intraoperative Registration during Neurosurgery.

Nazim Haouchine, Reuben Dorent, Parikshit Juvekar, Erickson Torio, William M Wells, Tina Kapur, Alexandra J Golby, Sarah Frisken
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Abstract

We present a novel method for intraoperative patient-to-image registration by learning Expected Appearances. Our method uses preoperative imaging to synthesize patient-specific expected views through a surgical microscope for a predicted range of transformations. Our method estimates the camera pose by minimizing the dissimilarity between the intraoperative 2D view through the optical microscope and the synthesized expected texture. In contrast to conventional methods, our approach transfers the processing tasks to the preoperative stage, reducing thereby the impact of low-resolution, distorted, and noisy intraoperative images, that often degrade the registration accuracy. We applied our method in the context of neuronavigation during brain surgery. We evaluated our approach on synthetic data and on retrospective data from 6 clinical cases. Our method outperformed state-of-the-art methods and achieved accuracies that met current clinical standards.

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学习神经外科手术中术中注册的预期外观
我们提出了一种通过学习预期外观进行术中患者与图像配准的新方法。我们的方法利用术前成像,通过手术显微镜合成患者特定的预期视图,以预测变换范围。我们的方法通过最小化术中光学显微镜二维视图与合成的预期纹理之间的差异来估计相机姿态。与传统方法不同的是,我们的方法将处理任务转移到术前阶段,从而减少了低分辨率、扭曲和嘈杂的术中图像的影响,因为这些图像通常会降低配准精度。我们将这种方法应用于脑外科手术中的神经导航。我们在合成数据和 6 个临床病例的回顾性数据上对我们的方法进行了评估。我们的方法优于最先进的方法,并达到了目前的临床标准。
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