Imperial College London
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DrStamatiaGiannarou

Faculty of Medicine, Department of Surgery & Cancer

Senior Lecturer
 
 
 
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Contact

 

+44 (0)20 7594 3492stamatia.giannarou Website

 
 
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Location

 

413Bessemer BuildingSouth Kensington Campus

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Summary

 

Publications

Citation

BibTex format

@inproceedings{Huang:2022:10.1007/978-3-031-16449-1_2,
author = {Huang, B and Zheng, J-Q and Nguyen, A and Xu, C and Gkouzionis, I and Vyas, K and Tuch, D and Giannarou, S and Elson, DS},
doi = {10.1007/978-3-031-16449-1_2},
pages = {13--22},
publisher = {SPRINGER INTERNATIONAL PUBLISHING AG},
title = {Self-supervised depth estimation in laparoscopic image using 3D geometric consistency},
url = {http://dx.doi.org/10.1007/978-3-031-16449-1_2},
year = {2022}
}
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RIS format (EndNote, RefMan)

TY  - CPAPER
AB  - 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.
AU  - Huang,B
AU  - Zheng,J-Q
AU  - Nguyen,A
AU  - Xu,C
AU  - Gkouzionis,I
AU  - Vyas,K
AU  - Tuch,D
AU  - Giannarou,S
AU  - Elson,DS
DO  - 10.1007/978-3-031-16449-1_2
EP  - 22
PB  - SPRINGER INTERNATIONAL PUBLISHING AG
PY  - 2022///
SN  - 0302-9743
SP  - 13
TI  - Self-supervised depth estimation in laparoscopic image using 3D geometric consistency
UR  - http://dx.doi.org/10.1007/978-3-031-16449-1_2
UR  - https://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=PARTNER_APP&SrcAuth=LinksAMR&KeyUT=WOS:000867568000002&DestLinkType=FullRecord&DestApp=ALL_WOS&UsrCustomerID=a2bf6146997ec60c407a63945d4e92bb
UR  - https://link.springer.com/chapter/10.1007/978-3-031-16449-1_2
ER  -
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