Self-supervised generative adverrsarial network for depth estimation in laparoscopic images
File(s)
Author(s)
Type
Conference Paper
Abstract
Dense depth estimation and 3D reconstruction of a surgical scene are crucial steps in computer assisted surgery. Recent work has shown that depth estimation from a stereo image pair could be solved with convolutional neural networks. However, most recent depth estimation models were trained on datasets with per-pixel ground truth. Such data is especially rare for laparoscopic imaging, making it hard to apply supervised depth estimation to real surgical applications. To overcome this limitation, we propose SADepth, a new self-supervised depth estimation method based on Generative Adversarial Networks. It consists of an encoder-decoder generator and a discriminator to incorporate geometry constraints during training. Multi-scale outputs from the generator help to solve the local minima caused by the photometric reprojection loss, while the adversarial learning improves the framework generation quality. Extensive experiments on two public datasets show that SADepth outperforms recent state-of-the-art unsupervised methods by a large margin, and reduces the gap between supervised and unsupervised depth estimation in laparoscopic images.
Editor(s)
DeBruijne, M
Cattin, PC
Cotin, S
Padoy, N
Speidel, S
Zheng, Y
Essert, C
Date Issued
2021-09-19
Date Acceptance
2021-09-01
Citation
Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention, 2021, 12904, pp.227-237
ISBN
978-3-030-87201-4
Publisher
Springer
Start Page
227
End Page
237
Journal / Book Title
Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention
Volume
12904
Copyright Statement
© Springer Nature Switzerland AG 2021
Sponsor
Cancer Research UK
Imperial College Healthcare NHS Trust- BRC Funding
National Institute for Health Research
Identifier
http://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcApp=PARTNER_APP&SrcAuth=LinksAMR&KeyUT=WOS:000712021400022&DestLinkType=FullRecord&DestApp=ALL_WOS&UsrCustomerID=1ba7043ffcc86c417c072aa74d649202
Grant Number
25147
RDB04
NIHR200035
Source
International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI)
Subjects
Science & Technology
Technology
Life Sciences & Biomedicine
Computer Science, Artificial Intelligence
Computer Science, Software Engineering
Engineering, Biomedical
Imaging Science & Photographic Technology
Radiology, Nuclear Medicine & Medical Imaging
Surgery
Computer Science
Engineering
Depth estimation
Laparoscopic images
Generative adversarial network
Publication Status
Published
Start Date
2021-09-27
Finish Date
2021-10-01
Coverage Spatial
ELECTR NETWORK