Augmented reality navigation for liver resection with a stereoscopic laparoscope. (April 2020)

• Record Type: Journal Article
• Authors: Luo, Huoling; Yin, Dalong; Zhang, Shugeng; Xiao, Deqiang; He, Baochun; Meng, Fanzheng; Zhang, Yanfang; Cai, Wei; He, Shenghao; Zhang, Wenyu; Hu, Qingmao; Guo, Hongrui; Liang, Shuhang; Zhou, Shuo; Liu, Shuxun; Sun, Linmao; Guo, Xiao; Fang, Chihua; Liu, Lianxin; Jia, Fucang
• Is Part Of: Computer methods and programs in biomedicine. Volume 187(2020)
• Publication Date: 2020-04
• Subjects: Augmented reality -- Laparoscopic surgery -- Liver resection -- Surgical navigation; Medicine -- Computer programs -- Periodicals; Biology -- Computer programs -- Periodicals; Computers -- Periodicals; Medicine -- Periodicals; Médecine -- Logiciels -- Périodiques; Biologie -- Logiciels -- Périodiques; Biology -- Computer programs; Medicine -- Computer programs; Periodicals; Electronic journals; 610.28
• DOI: 10.1016/j.cmpb.2019.105099
• Languages: English
• ISSNs: 0169-2607

Abstract

Highlights: Augmented reality techniques can help surgeons to see the internal anatomy from laparoscopic video images. Deep learning methods are successfully used in dense stereo reconstructions of liver surfaces and in liver segmentations of preoperative CT images. The augmented reality prototype system was validated ex vivo and in vivo and the accuracy is comparable to the state-of-the-art. Abstract: Objective: Understanding the three-dimensional (3D) spatial position and orientation of vessels and tumor(s) is vital in laparoscopic liver resection procedures. Augmented reality (AR) techniques can help surgeons see the patient's internal anatomy in conjunction with laparoscopic video images. Method: In this paper, we present an AR-assisted navigation system for liver resection based on a rigid stereoscopic laparoscope. The stereo image pairs from the laparoscope are used by an unsupervised convolutional network (CNN) framework to estimate depth and generate an intraoperative 3D liver surface. Meanwhile, 3D models of the patient's surgical field are segmented from preoperative CT images using V-Net architecture for volumetric image data in an end-to-end predictive style. A globally optimal iterative closest point (Go-ICP) algorithm is adopted to register the pre- and intraoperative models into a unified coordinate space; then, the preoperative 3D models are superimposed on the live laparoscopic images to provide the surgeon with detailed information about the subsurface of the patient's anatomy, including tumors, their resection margins and vessels. Results: The proposed navigation system is tested on four laboratory ex vivo porcine livers and five operating theatre in vivo porcine experiments to validate its accuracy. The ex vivo and in vivo reprojection errors (RPE) are 6.04 ± 1.85 mm and 8.73 ± 2.43 mm, respectively. Conclusion and Significance: Both the qualitative and quantitative results indicate that our AR-assisted navigation system shows promise and has the potential to be highly useful in clinical practice.