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A monocular SLAM system based on SIFT features for gastroscope tracking

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Abstract

During flexible gastroscopy, physicians have extreme difficulties to self-localize. Camera tracking method such as simultaneous localization and mapping (SLAM) has become a research hotspot in recent years, allowing tracking of the endoscope. However, most of the existing solutions have focused on tasks in which sufficient texture information is available, such as laparoscope tracking, and cannot be applied to gastroscope tracking since gastroscopic images have fewer textures than laparoscopic images. This paper proposes a new monocular SLAM framework based on scale-invariant feature transform (SIFT) and narrow-band imaging (NBI), which extracts SIFT features instead of oriented features from accelerated segment test (FAST) and rotated binary robust independent elementary features (BRIEF) features from gastroscopic NBI images, and performs feature retention based on the response sorting strategy for achieving more matches. Experimental results show that the root mean squared error of the proposed algorithm can reach a minimum of 2.074 mm, and the pose accuracy can be improved by up to 25.73% compared with oriented FAST and rotated BRIEF (ORB)-SLAM. SIFT features and response sorting strategy can achieve more accurate matching in gastroscopic NBI images than other features and homogenization strategy, and the proposed algorithm can also run successfully on real clinical gastroscopic data. The proposed algorithm has the potential clinical value to assist physicians in locating the gastroscope during gastroscopy.

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Funding

This work was supported by the National Key R&D Program of China under Grant 2019YFB1311501 and in part by the National Natural Science Foundation of China under Grant 62133010.

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Authors and Affiliations

  1. Key Laboratory of Mechanism Theory and Equipment Design of Ministry of Education, Tianjin University, Tianjin, China

    Yifan Wang, Lun Gong & Siyang Zuo

  2. Faculty of Engineering and Information Technology, Robotics Institute, University of Technology Sydney, Sydney, Australia

    Liang Zhao

  3. Tianjin Medical University General Hospital, Tianjin, China

    Xin Chen

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  1. Yifan Wang
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  2. Liang Zhao
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  3. Lun Gong
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  4. Xin Chen
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  5. Siyang Zuo
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Correspondence to Siyang Zuo.

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Ethics approval

This study was approved by the Ethical Committee of Tianjin Medical University General Hospital, and the approval ID is IRB2022-YX-046–01.

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Informed consent was obtained from all individual participants included in the study.

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Wang, Y., Zhao, L., Gong, L. et al. A monocular SLAM system based on SIFT features for gastroscope tracking. Med Biol Eng Comput 61, 511–523 (2023). https://doi.org/10.1007/s11517-022-02739-1

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  • DOI: https://doi.org/10.1007/s11517-022-02739-1

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