Self-supervised Depth Estimation in Laparoscopic Image Using 3D Geometric Consistency

Huang, Baoru, Zheng, Jian-Qing, Nguyen, Anh ORCID: 0000-0002-1449-211X, Xu, Chi, Gkouzionis, Ioannis, Vyas, Kunal, Tuch, David, Giannarou, Stamatia and Elson, Daniel S (2022) Self-supervised Depth Estimation in Laparoscopic Image Using 3D Geometric Consistency. .

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Official URL: http://dx.doi.org/10.1007/978-3-031-16449-1_2

Abstract

Depth estimation is a crucial step for image-guided intervention in robotic surgery and laparoscopic imaging system. Since per-pixel depth ground truth is difficult to acquire for laparoscopic image data, it is rarely possible to apply supervised depth estimation to surgical applications. As an alternative, self-supervised methods have been introduced to train depth estimators using only synchronized stereo image pairs. However, most recent work focused on the left-right consistency in 2D and ignored valuable inherent 3D information on the object in real world coordinates, meaning that the left-right 3D geometric structural consistency is not fully utilized. To overcome this limitation, we present M3Depth, a self-supervised depth estimator to leverage 3D geometric structural information hidden in stereo pairs while keeping monocular inference. The method also removes the influence of border regions unseen in at least one of the stereo images via masking, to enhance the correspondences between left and right images in overlapping areas. Extensive experiments show that our method outperforms previous self-supervised approaches on both a public dataset and a newly acquired dataset by a large margin, indicating a good generalization across different samples and laparoscopes.

Item Type:	Conference or Workshop Item (Unspecified)
Uncontrolled Keywords:	3D geometric consistency, Laparoscopic images, Self-supervised monocular depth estimation
Divisions:	Faculty of Science and Engineering > School of Electrical Engineering, Electronics and Computer Science
Depositing User:	Symplectic Admin
Date Deposited:	06 Mar 2023 11:33
Last Modified:	06 Mar 2023 11:33
DOI:	10.1007/978-3-031-16449-1_2
Open Access URL:	https://doi.org/10.48550/arXiv.2208.08407
Related URLs:	Author Publisher
URI:	https://livrepository.liverpool.ac.uk/id/eprint/3168786