Unsupervised binocular depth prediction network for laparoscopic surgery

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.

Keywords:

• Depth estimation
• 3D reconstruction
• laparoscopic surgery
• unsupervised learning

Additional information

Funding

This study was supported by the National Key Research and Development Program [Nos. 2016YFC0106500/2 and SQ2017ZY040217/03], the NSFC-Guangdong Union Grant [No. U1401254], the NSFC-Shenzhen Union Grant [No. U1613221], the Guangdong Scientific and Technology Program [No. 2015B020214005], the Shenzhen Key Basic Science Program [No. JCYJ20170413162213765], and the Shenzhen Key Laboratory Project under Grant ZDSYS201707271637577.