Enhanced self-supervised monocular depth estimation with self-attention and joint depth-pose loss for laparoscopic images
This study considers the characteristics of laparoscopic datasets and presents a simple yet effective self-supervised monocular depth estimation. We propose a joint depth-pose loss function based on the extracted feature for depth estimation on laparoscopic images guided by a self-attention block. The experimental results prove that all of the proposed components contribute to the proposed method. Furthermore, the proposed method strikes an efficient balance between computational efficiency and performance.
Authors
- Takayuki Kitasaka (ORCID: https://orcid.org/0000-0003-1648-1999)
- Kazunari Misawa (ORCID: https://orcid.org/0000-0002-2047-3919)
- Kensaku Mori (ORCID: https://orcid.org/0000-0002-0100-4797)
- Masahiro Oda (ORCID: https://orcid.org/0000-0001-7714-422X)
- Wenda Li (ORCID: https://orcid.org/0000-0003-4486-7207)
- Yuichiro Hayashi
Institutions
- National Institute of Informatics (JP)
- Aichi Institute of Technology (JP)
- Aichi Cancer Center (JP)
- Nagoya University (JP)
Publication Details
- Journal
- International Journal of Computer Assisted Radiology and Surgery
- Published
- 2025-02-28
- DOI
- https://doi.org/10.1007/s11548-025-03332-1
- Citations
- 3
- Primary Topic
- Advanced Vision and Imaging
- Type
- article
- Field-Weighted Citation Impact
- 3.83