Self-supervised depth estimation with uncertainty-weight joint loss function based on laparoscopic videos

Wenda Li; Yuichiro Hayashi; Masahiro Oda; Takayuki Kitasaka; Kazunari Misawa; Kensaku Mori

doi:10.1117/12.2612829

4 April 2022 Self-supervised depth estimation with uncertainty-weight joint loss function based on laparoscopic videos

Wenda Li, Yuichiro Hayashi, Masahiro Oda, Takayuki Kitasaka, Kazunari Misawa, Kensaku Mori

Proceedings Volume 12034, Medical Imaging 2022: Image-Guided Procedures, Robotic Interventions, and Modeling; 120342V (2022) https://doi.org/10.1117/12.2612829
Event: SPIE Medical Imaging, 2022, San Diego, California, United States

Conference Poster

Abstract

In this paper, we propose a self-supervised depth estimation with uncertainty-weight joint loss function based on laparoscopic videos. Although self-supervised learning has achieved impressive performance in depth estimation using pose estimation as an auxiliary task, it still shows undesired results for the pose estimation. Different from streetscape datasets, the laparoscope motion is limited by the minimally invasive surgery settings. It is challenging to estimate laparoscopes’ poses with complex rotations from RGB images. To address this issue, we propose an improved self-supervised depth estimation method with relative pose loss for laparoscopic videos. Furthermore, we adopt homoscedastic uncertainty to weigh our loss function to balance each subtask. In addition to the evaluation for known datasets, we also tested the generalization ability of our proposed method using known datasets and unseen datasets. The experimental results showed that our proposed method outperforms baseline for depth estimation and pose estimation on known datasets and had competitive results on unseen datasets. For depth estimation, the proposed method had about 10.5% improvement on RMSE evaluation compared to the baseline.

Conference Presentation

Citation Download Citation

Wenda Li, Yuichiro Hayashi, Masahiro Oda, Takayuki Kitasaka, Kazunari Misawa, and Kensaku Mori "Self-supervised depth estimation with uncertainty-weight joint loss function based on laparoscopic videos", Proc. SPIE 12034, Medical Imaging 2022: Image-Guided Procedures, Robotic Interventions, and Modeling, 120342V (4 April 2022); https://doi.org/10.1117/12.2612829

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