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Study on method of organ section retention and tracking through deep learning in automated diagnostic and therapeutic robotics

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International Journal of Computer Assisted Radiology and Surgery Aims and scope Submit manuscript

Abstract

Purpose

In high-intensity focused ultrasound (HIFU) treatment of the kidney and liver, tracking the organs is essential because respiratory motions make continuous cauterization of the affected area difficult and may cause damage to other parts of the body. In this study, we propose a tracking system for rotational scanning, and propose and evaluate a method for estimating the angles of organs in ultrasound images.

Methods

We proposed AEMA, AEMAD, and AEMAD++ as methods for estimating the angles of organs in ultrasound images, using RUDS and a phantom to acquire 90-degree images of a kidney from the long-axis image to the short-axis image as a data set. Six datasets were used, with five for preliminary preparation and one for testing, while the initial position was shifted by 2 mm in the contralateral axis direction. The test data set was evaluated by estimating the angle using each method.

Results

The accuracy and processing speed of angle estimation for AEMA, AEMAD, and AEMAD++ were 23.8% and 0.33 FPS for AEMAD, 32.0% and 0.56 FPS for AEMAD, and 29.5% and 3.20 FPS for AEMAD++, with tolerance of ± 2.5 degrees. AEMAD++ offered the best speed and accuracy.

Conclusion

In the phantom experiment, AEMAD++ showed the effectiveness of tracking the long-axis image of the kidney in rotational scanning. In the future, we will add either the area of surrounding organs or the internal structure of the kidney as a new feature to validate the results.

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Acknowledgements

The authors gratefully acknowledge the support by Hideyo Miyazaki (Center Hospital of the National Center for Global Health and Medicine), Hideyuki Iijima, Toshiyuki Iwai, Hidetoshi Nagaoka (Obayashi Mfg. Co., Ltd.), the financial support by JSPS KAKENHI Grant Number JP20H02113 and the Saitama Prefecture New Technology and Product Development Subsidy Project.

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Authors and Affiliations

  1. The University of Electro-Communications, 1-5-1 Chofugaoka, Chofu, Tokyo, Japan

    Takumi Fujibayashi, Norihiro Koizumi, Yu Nishiyama, Yusuke Watanabe, Jiayi Zhou, Momoko Matsuyama & Miyu Yamada

  2. National Institute of Advanced Industrial Science and Technology (AIST), 1-2-1 Namiki, Tsukuba, Japan

    Ryosuke Tsumura & Kiyoshi Yoshinaka

  3. Nihon University, 1-6 Kandasurugadai, Chiyoda-Ku, Tokyo, Japan

    Naoki Matsumoto

  4. The University of Tokyo, 7-3-1 Hongo, Bunkyo-Ku, Tokyo, Japan

    Hiroyuki Tsukihara

  5. Yokohama City University Medical Center, 4-57 Urafune-cho, Minami-ku, Yokohama, Kanagawa, Japan

    Kazushi Numata

Authors
  1. Takumi Fujibayashi
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  2. Norihiro Koizumi
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  10. Naoki Matsumoto
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Corresponding author

Correspondence to Norihiro Koizumi.

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Fujibayashi, T., Koizumi, N., Nishiyama, Y. et al. Study on method of organ section retention and tracking through deep learning in automated diagnostic and therapeutic robotics. Int J CARS 18, 2101–2109 (2023). https://doi.org/10.1007/s11548-023-02955-6

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  • DOI: https://doi.org/10.1007/s11548-023-02955-6

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