Unsupervised binocular depth prediction network for laparoscopic surgery

• Main Authors: Ke Xu, Zhiyong Chen, Fucang Jia
• Format: Article
• Language: English
• Published: Taylor & Francis Group 2019-10-01
• Series: Computer Assisted Surgery
• Subjects: Depth estimation; 3D reconstruction; laparoscopic surgery; unsupervised learning
• Online Access: http://dx.doi.org/10.1080/24699322.2018.1557889

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.