%0 Journal Article
%T Unsupervised binocular depth prediction network for laparoscopic surgery
%A Fucang Jia
%A Ke Xu
%A Zhiyong Chen
%J Computer Assisted Surgery
%D 2019
%R https://doi.org/10.1080/24699322.2018.1557889
%X Abstract 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£żD 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£żD 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
%U https://www.tandfonline.com/doi/full/10.1080/24699322.2018.1557889