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Geometric Constraints for Self-supervised Monocular Depth Estimation on Laparoscopic Images with Dual-task Consistency

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Medical Image Computing and Computer Assisted Intervention – MICCAI 2022 (MICCAI 2022)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13434))

Abstract

Depth values are essential information to automate surgical robots and achieve Augmented Reality technology for minimally invasive surgery. Although depth-pose self-supervised monocular depth estimation performs impressively for autonomous driving scenarios, it is more challenging to predict accurate depth values for laparoscopic images due to the following two aspects: (i) the laparoscope’s motions contain many rotations, leading to pose estimation difficulties for the depth-pose learning strategy; (ii) the smooth surface reduces photometric error even if the matching pixels are inaccurate between adjacent frames. This paper proposes a novel self-supervised monocular depth estimation for laparoscopic images with geometric constraints. We predict the scene coordinates as an auxiliary task and construct dual-task consistency between the predicted depth maps and scene coordinates under a unified camera coordinate system to achieve pixel-level geometric constraints. We extend the pose estimation into a Siamese process to provide stronger and more balanced geometric constraints in a depth-pose learning strategy by leveraging the order of the adjacent frames in a video sequence. We also design a weight mask for depth estimation based on our consistency to alleviate the interference from predictions with low confidence. The experimental results showed that the proposed method outperformed the baseline on depth and pose estimation. Our code is available at https://github.com/MoriLabNU/GCDepthL.

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Acknowledgments

The authors are grateful for the support from JST CREST Grant Number JPMJCR20D5; MEXT/JSPS KAKENHI Grant Numbers 17H00867, 26108006, and 21K19898; JSPS Bilateral International Collaboration Grants; and CIBoG program of Nagoya University from the MEXT WISE program.

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Authors and Affiliations

  1. Graduate School of Informatics, Nagoya University, Furou-cho, Chikusa-ku, Nagoya, Aichi, 464-8601, Japan

    Wenda Li, Yuichiro Hayashi, Masahiro Oda & Kensaku Mori

  2. Information and Communications, Nagoya University, Furou-cho, Chikusa-ku, Nagoya, Aichi, 464-8601, Japan

    Masahiro Oda

  3. Faculty of Information Science, Aichi Institute of Technology, Yakusacho, Toyota, Aichi, 470-0392, Japan

    Takayuki Kitasaka

  4. Aichi Cancer Center Hospital, Chikusa-ku, Nagoya, Aichi, 464-8681, Japan

    Kazunari Misawa

  5. Information Technology Center, Nagoya University, Furou-cho, Chikusa-ku, Nagoya, Aichi, 464-8601, Japan

    Kensaku Mori

  6. Research Center of Medical Bigdata, National Institute of Informatics, Hitotsubashi, Chiyoda-ku, Tokyo, 101-8430, Japan

    Kensaku Mori

Authors
  1. Wenda Li
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  2. Yuichiro Hayashi
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  3. Masahiro Oda
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  4. Takayuki Kitasaka
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  5. Kazunari Misawa
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  6. Kensaku Mori
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Corresponding author

Correspondence to Kensaku Mori .

Editor information

Editors and Affiliations

  1. Rochester Institute of Technology, Rochester, NY, USA

    Linwei Wang

  2. Chinese University of Hong Kong, Hong Kong, Hong Kong

    Qi Dou

  3. University of Virginia, Charlottesville, VA, USA

    P. Thomas Fletcher

  4. National Center for Tumor Diseases (NCT/UCC), Dresden, Germany

    Stefanie Speidel

  5. Case Western Reserve University, Cleveland, OH, USA

    Shuo Li

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Li, W., Hayashi, Y., Oda, M., Kitasaka, T., Misawa, K., Mori, K. (2022). Geometric Constraints for Self-supervised Monocular Depth Estimation on Laparoscopic Images with Dual-task Consistency. In: Wang, L., Dou, Q., Fletcher, P.T., Speidel, S., Li, S. (eds) Medical Image Computing and Computer Assisted Intervention – MICCAI 2022. MICCAI 2022. Lecture Notes in Computer Science, vol 13434. Springer, Cham. https://doi.org/10.1007/978-3-031-16440-8_45

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  • DOI: https://doi.org/10.1007/978-3-031-16440-8_45

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-16439-2

  • Online ISBN: 978-3-031-16440-8

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