Unsupervised binocular depth prediction network for laparoscopic surgery. (1st October 2019)

• Record Type: Journal Article
• Authors: Xu, Ke; Chen, Zhiyong; Jia, Fucang
• Is Part Of: Computer assisted surgery. Volume 24(2019)Supplement 1
• Page Start: 30
• Page End: 35
• Publication Date: 2019-10-01
• Subjects: Depth estimation -- 3D reconstruction -- laparoscopic surgery -- unsupervised learning; Computer-assisted surgery -- Periodicals
• DOI: 10.1080/24699322.2018.1557889
• Languages: English
• ISSNs: 2469-9322

Abstract

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 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.