Zhang Xiaohui, Ji Xuquan, Wang Junchen, Fan Yubo, Tao Chunjing
School of Engineering Medicine, Beijing Advanced Innovation Centre for Biomedical Engineering, Beihang University, No. 37 Xueyuan Road, Haidian District, Beijing, 100083 China.
School of Biomedical Engineering, Beihang University, No. 37 Xueyuan Road, Haidian District, Beijing, 100083 China.
Biomed Eng Lett. 2023 Feb 1;13(2):165-174. doi: 10.1007/s13534-023-00263-1. eCollection 2023 May.
An unpredictable dynamic surgical environment makes it necessary to measure morphological information of target tissue real-time for laparoscopic image-guided navigation. The stereo vision method for intraoperative tissue 3D reconstruction has the most potential for clinical development benefiting from its high reconstruction accuracy and laparoscopy compatibility. However, existing stereo vision methods have difficulty in achieving high reconstruction accuracy in real time. Also, intraoperative tissue reconstruction results often contain complex background and instrument information that prevents clinical development for image-guided systems. Taking laparoscopic partial nephrectomy (LPN) as the research object, this paper realizes a real-time dense reconstruction and extraction of the kidney tissue surface. The central symmetrical Census based semi-global block stereo matching algorithm is proposed to generate a dense disparity map. A GPU-based pixel-by-pixel connectivity segmentation mechanism is designed to segment the renal tissue area. An in-vitro porcine heart, in-vivo porcine kidney and offline clinical LPN data were performed to evaluate the accuracy and effectiveness of our approach. The algorithm achieved a reconstruction accuracy of ± 2 mm with a real-time update rate of 21 fps for an HD image size of 960 × 540, and 91.0% target tissue segmentation accuracy even with surgical instrument occlusions. Experimental results have demonstrated that the proposed method could accurately reconstruct and extract renal surface in real-time in LPN. The measurement results can be used directly for image-guided systems. Our method provides a new way to measure geometric information of target tissue intraoperatively in laparoscopy surgery.
The online version contains supplementary material available at 10.1007/s13534-023-00263-1.
不可预测的动态手术环境使得在腹腔镜图像引导导航中实时测量目标组织的形态信息成为必要。术中组织三维重建的立体视觉方法因其高重建精度和腹腔镜兼容性而具有最大的临床发展潜力。然而，现有的立体视觉方法难以实时实现高重建精度。此外，术中组织重建结果通常包含复杂的背景和器械信息，这阻碍了图像引导系统的临床发展。本文以腹腔镜部分肾切除术（LPN）为研究对象，实现了肾脏组织表面的实时密集重建和提取。提出了基于中心对称 Census 的半全局块立体匹配算法来生成密集视差图。设计了基于 GPU 的逐像素连通性分割机制来分割肾组织区域。对体外猪心脏、体内猪肾脏和离线临床 LPN 数据进行了实验，以评估我们方法的准确性和有效性。对于 960×540 的高清图像尺寸，该算法实现了±2 毫米的重建精度和 21 帧/秒的实时更新率，即使在手术器械遮挡的情况下，目标组织分割准确率也达到了 91.0%。实验结果表明，所提出的方法能够在 LPN 中实时准确地重建和提取肾脏表面。测量结果可直接用于图像引导系统。我们的方法为腹腔镜手术中术中测量目标组织的几何信息提供了一种新途径。
在线版本包含可在 10.1007/s13534-023-00263-1 上获取的补充材料。
