Learning Expected Appearances for Intraoperative Registration during Neurosurgery.
Learning Expected Appearances for Intraoperative Registration during Neurosurgery.
复制标题
了解神经外科手术中术中注册的预期外观。
DOI：
10.1007/978-3-031-43996-4_22
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发表时间：
2023
期刊：
影响因子：
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通讯作者：
Frisken,Sarah
中科院分区：
文献类型：
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作者：
Haouchine,Nazim;Dorent,Reuben;Juvekar,Parikshit;Torio,Erickson;Wells3rd,WilliamM;Kapur,Tina;Golby,AlexandraJ;Frisken,Sarah
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颖方法。我们的方法使用术前成像通过手术显微镜合成患者特定的预期视图，以预测转换范围。我们的方法通过最小化通过光学显微镜的术中 2D 视图与合成的预期纹理之间的差异来估计相机姿态。与传统方法相比，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、扭曲和噪声的术中图像的影响，这些图像通常会降低配准精度。我们将我们的方法应用于脑部手术期间的神经导航。我们根据 6 个临床病例的综合数据和回顾性数据评估了我们的方法。我们的方法优于最先进的方法，并达到了满足当前临床标准的准确性。
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.