Enhancing Bronchoscopy Depth Estimation through Synthetic-to-Real Domain Adaptation

Bibliographic Details
Title: Enhancing Bronchoscopy Depth Estimation through Synthetic-to-Real Domain Adaptation
Authors: Tian, Qingyao, Liao, Huai, Huang, Xinyan, Li, Lujie, Liu, Hongbin
Publication Year: 2024
Collection: Computer Science
Subject Terms: Electrical Engineering and Systems Science - Image and Video Processing, Computer Science - Computer Vision and Pattern Recognition
More Details: Monocular depth estimation has shown promise in general imaging tasks, aiding in localization and 3D reconstruction. While effective in various domains, its application to bronchoscopic images is hindered by the lack of labeled data, challenging the use of supervised learning methods. In this work, we propose a transfer learning framework that leverages synthetic data with depth labels for training and adapts domain knowledge for accurate depth estimation in real bronchoscope data. Our network demonstrates improved depth prediction on real footage using domain adaptation compared to training solely on synthetic data, validating our approach.
Document Type: Working Paper
Access URL: http://arxiv.org/abs/2411.04404
Accession Number: edsarx.2411.04404
Database: arXiv
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  Data: Enhancing Bronchoscopy Depth Estimation through Synthetic-to-Real Domain Adaptation
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  Data: <searchLink fieldCode="AR" term="%22Tian%2C+Qingyao%22">Tian, Qingyao</searchLink><br /><searchLink fieldCode="AR" term="%22Liao%2C+Huai%22">Liao, Huai</searchLink><br /><searchLink fieldCode="AR" term="%22Huang%2C+Xinyan%22">Huang, Xinyan</searchLink><br /><searchLink fieldCode="AR" term="%22Li%2C+Lujie%22">Li, Lujie</searchLink><br /><searchLink fieldCode="AR" term="%22Liu%2C+Hongbin%22">Liu, Hongbin</searchLink>
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  Data: Monocular depth estimation has shown promise in general imaging tasks, aiding in localization and 3D reconstruction. While effective in various domains, its application to bronchoscopic images is hindered by the lack of labeled data, challenging the use of supervised learning methods. In this work, we propose a transfer learning framework that leverages synthetic data with depth labels for training and adapts domain knowledge for accurate depth estimation in real bronchoscope data. Our network demonstrates improved depth prediction on real footage using domain adaptation compared to training solely on synthetic data, validating our approach.
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      – SubjectFull: Electrical Engineering and Systems Science - Image and Video Processing
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      – SubjectFull: Computer Science - Computer Vision and Pattern Recognition
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      – TitleFull: Enhancing Bronchoscopy Depth Estimation through Synthetic-to-Real Domain Adaptation
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            NameFull: Tian, Qingyao
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            NameFull: Liao, Huai
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            NameFull: Huang, Xinyan
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            NameFull: Li, Lujie
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            NameFull: Liu, Hongbin
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              M: 11
              Type: published
              Y: 2024
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