Unsupervised binocular depth prediction network for laparoscopic surgery

Computer Assisted Surgery

Zhiyong Chen¹  Ke Xu²  Fucang Jia³ 

[1] School of Computer Science and Information Security, Guilin University of Electronic Technology, Guilin, China;School of Computer Science and Information Security, Guilin University of Electronic Technology, Guilin, China;Shenzhen Key Laboratory of Minimally Invasive Surgical Robotics and System, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, Chin;Shenzhen Key Laboratory of Minimally Invasive Surgical Robotics and System, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, Chin;

关键词: Depth estimation;    3D reconstruction;    laparoscopic surgery;    unsupervised learning;   

DOI  :  10.1080/24699322.2018.1557889

来源: publisher

【 摘 要 】

Minimally invasive laparoscopic surgery is associated with small wounds and short recovery time, reducing postoperative infections. Traditional two-dimensional (2D) laparoscopic imaging lacks depth perception and does not provide quantitative depth information, thereby limiting the field of vision and operation during surgery. However, three-dimensional (3D) laparoscopic imaging from 2 D images lets surgeons have a depth perception. However, the depth information is not quantitative and cannot be used for robotic surgery. Therefore, this study aimed to reconstruct the accurate depth map for binocular 3 D laparoscopy. In this study, an unsupervised learning method was proposed to calculate the accurate depth while the ground-truth depth was not available. Experimental results proved that the method not only generated accurate depth maps but also provided real-time computation, and it could be used in minimally invasive robotic surgery.

【 授权许可】

CC BY