Adversarial Domain Feature Adaptation for Bronchoscopic Depth Estimation
Karaoglu MA, Brasch N, Stollenga M, Wein W, Navab N, Tombari F, Ladikos A (2021)
Publication Type: Conference contribution
Publication year: 2021
Journal
Publisher: Springer Science and Business Media Deutschland GmbH
Book Volume: 12904 LNCS
Pages Range: 300-310
Conference Proceedings Title: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Event location: Virtual, Online
ISBN: 9783030872014
DOI: 10.1007/978-3-030-87202-1_29
Abstract
Depth estimation from monocular images is an important task in localization and 3D reconstruction pipelines for bronchoscopic navigation. Various supervised and self-supervised deep learning-based approaches have proven themselves on this task for natural images. However, the lack of labeled data and the bronchial tissue’s feature-scarce texture make the utilization of these methods ineffective on bronchoscopic scenes. In this work, we propose an alternative domain-adaptive approach. Our novel two-step structure first trains a depth estimation network with labeled synthetic images in a supervised manner; then adopts an unsupervised adversarial domain feature adaptation scheme to improve the performance on real images. The results of our experiments show that the proposed method improves the network’s performance on real images by a considerable margin and can be employed in 3D reconstruction pipelines.
Involved external institutions
Germany (DE)How to cite
APA:
Karaoglu, M.A., Brasch, N., Stollenga, M., Wein, W., Navab, N., Tombari, F., & Ladikos, A. (2021). Adversarial Domain Feature Adaptation for Bronchoscopic Depth Estimation. In Marleen de Bruijne, Marleen de Bruijne, Philippe C. Cattin, Stéphane Cotin, Nicolas Padoy, Stefanie Speidel, Yefeng Zheng, Caroline Essert (Eds.), Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (pp. 300-310). Virtual, Online: Springer Science and Business Media Deutschland GmbH.
MLA:
Karaoglu, Mert Asim, et al. "Adversarial Domain Feature Adaptation for Bronchoscopic Depth Estimation." Proceedings of the 24th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2021, Virtual, Online Ed. Marleen de Bruijne, Marleen de Bruijne, Philippe C. Cattin, Stéphane Cotin, Nicolas Padoy, Stefanie Speidel, Yefeng Zheng, Caroline Essert, Springer Science and Business Media Deutschland GmbH, 2021. 300-310.
BibTeX: Download