The Hamlyn Centre for Robotic Surgery, Department of Surgery and Cancer, Imperial College London, London, SW7 2AZ, UK.
Lightpoint Medical Ltd, Chesham, UK.
Int J Comput Assist Radiol Surg. 2020 Aug;15(8):1389-1397. doi: 10.1007/s11548-020-02205-z. Epub 2020 Jun 16.
In surgical oncology, complete cancer resection and lymph node identification are challenging due to the lack of reliable intraoperative visualization. Recently, endoscopic radio-guided cancer resection has been introduced where a novel tethered laparoscopic gamma detector can be used to determine the location of tracer activity, which can complement preoperative nuclear imaging data and endoscopic imaging. However, these probes do not clearly indicate where on the tissue surface the activity originates, making localization of pathological sites difficult and increasing the mental workload of the surgeons. Therefore, a robust real-time gamma probe tracking system integrated with augmented reality is proposed.
A dual-pattern marker has been attached to the gamma probe, which combines chessboard vertices and circular dots for higher detection accuracy. Both patterns are detected simultaneously based on blob detection and the pixel intensity-based vertices detector and used to estimate the pose of the probe. Temporal information is incorporated into the framework to reduce tracking failure. Furthermore, we utilized the 3D point cloud generated from structure from motion to find the intersection between the probe axis and the tissue surface. When presented as an augmented image, this can provide visual feedback to the surgeons.
The method has been validated with ground truth probe pose data generated using the OptiTrack system. When detecting the orientation of the pose using circular dots and chessboard dots alone, the mean error obtained is [Formula: see text] and [Formula: see text], respectively. As for the translation, the mean error for each pattern is 1.78 mm and 1.81 mm. The detection limits for pitch, roll and yaw are [Formula: see text] and [Formula: see text]-[Formula: see text]-[Formula: see text] .
The performance evaluation results show that this dual-pattern marker can provide high detection rates, as well as more accurate pose estimation and a larger workspace than the previously proposed hybrid markers. The augmented reality will be used to provide visual feedback to the surgeons on the location of the affected lymph nodes or tumor.
在外科肿瘤学中，由于缺乏可靠的术中可视化手段，完全切除癌症并识别淋巴结具有挑战性。最近，引入了内镜引导下的癌症切除术，其中可以使用新型系绳腹腔镜伽马探测器来确定示踪剂活性的位置，这可以补充术前核成像数据和内镜成像。然而，这些探头并不能清楚地表明活性源自组织表面的哪个位置，这使得定位病理部位变得困难，并增加了外科医生的心理工作量。因此，提出了一种与增强现实集成的强大实时伽马探头跟踪系统。
在伽马探头上附加了一种双重模式标记，该标记结合了棋盘顶点和圆形点，以提高检测精度。基于斑点检测和基于像素强度的顶点检测器同时检测两种模式，并用于估计探头的姿势。将时间信息纳入框架中以减少跟踪失败。此外，我们利用运动结构生成的 3D 点云来找到探头轴与组织表面的交点。当作为增强图像呈现时，这可以为外科医生提供视觉反馈。
该方法已使用 OptiTrack 系统生成的真实探头姿态数据进行了验证。单独使用圆形点和棋盘点检测姿态的方向时，获得的平均误差分别为[公式：见文本]和[公式：见文本]。对于平移，每个模式的平均误差为 1.78mm 和 1.81mm。俯仰、横滚和偏航的检测极限分别为[公式：见文本]和[公式：见文本]-[公式：见文本]-[公式：见文本]。
性能评估结果表明，与之前提出的混合标记相比，这种双重模式标记可以提供更高的检测率以及更准确的姿态估计和更大的工作空间。增强现实将用于向外科医生提供受影响的淋巴结或肿瘤位置的视觉反馈。
