Institute of Computer Graphics and Vision, Faculty of Computer Science and Biomedical Engineering, Graz University of Technology, Inffeldgasse 16, 8010, Graz, Austria.
Computer Algorithms for Medicine Laboratory, Graz, Austria.
J Digit Imaging. 2019 Dec;32(6):1008-1018. doi: 10.1007/s10278-019-00272-6.
As of common routine in tumor resections, surgeons rely on local examinations of the removed tissues and on the swiftly made microscopy findings of the pathologist, which are based on intraoperatively taken tissue probes. This approach may imply an extended duration of the operation, increased effort for the medical staff, and longer occupancy of the operating room (OR). Mixed reality technologies, and particularly augmented reality, have already been applied in surgical scenarios with positive initial outcomes. Nonetheless, these methods have used manual or marker-based registration. In this work, we design an application for a marker-less registration of PET-CT information for a patient. The algorithm combines facial landmarks extracted from an RGB video stream, and the so-called Spatial-Mapping API provided by the HMD Microsoft HoloLens. The accuracy of the system is compared with a marker-based approach, and the opinions of field specialists have been collected during a demonstration. A survey based on the standard ISO-9241/110 has been designed for this purpose. The measurements show an average positioning error along the three axes of (x, y, z) = (3.3 ± 2.3, - 4.5 ± 2.9, - 9.3 ± 6.1) mm. Compared with the marker-based approach, this shows an increment of the positioning error of approx. 3 mm along two dimensions (x, y), which might be due to the absence of explicit markers. The application has been positively evaluated by the specialists; they have shown interest in continued further work and contributed to the development process with constructive criticism.
在肿瘤切除中,外科医生通常依靠对切除组织的局部检查以及病理学家快速进行的显微镜检查,这些检查基于术中采集的组织探针。这种方法可能会延长手术时间、增加医护人员的工作量,并延长手术室(OR)的占用时间。混合现实技术,尤其是增强现实技术,已经在手术场景中得到了应用,并取得了积极的初步成果。然而,这些方法都使用了手动或基于标记的注册。在这项工作中,我们设计了一种用于患者正电子发射断层扫描-计算机断层扫描(PET-CT)信息的无标记注册的应用程序。该算法结合了从 RGB 视频流中提取的面部地标,以及由 HMD Microsoft HoloLens 提供的所谓的空间映射 API。我们将系统的准确性与基于标记的方法进行了比较,并在演示过程中收集了领域专家的意见。为此目的,我们设计了一个基于标准 ISO-9241/110 的调查。测量结果显示,三个轴(x、y、z)的平均定位误差为(3.3±2.3、-4.5±2.9、-9.3±6.1)mm。与基于标记的方法相比,这两个维度(x、y)的定位误差增加了约 3mm,这可能是由于缺少明确的标记。该应用程序得到了专家的积极评价;他们对进一步的工作表现出兴趣,并通过建设性的批评为开发过程做出了贡献。