Image Search Strategy via Visual Servoing for Robotic Kidney Ultrasound Imaging

Author:

Fujibayashi Takumi12,Koizumi Norihiro2ORCID,Nishiyama Yu2ORCID,Zhou Jiayi2,Tsukihara Hiroyuki3,Yoshinaka Kiyoshi1ORCID,Tsumura Ryosuke1ORCID

Affiliation:

1. Health and Medical Research Institute, National Institute of Advanced Industrial Science and Technology, 1-2-1 Namiki, Tsukuba, Ibaraki 205-8564, Japan

2. Graduate School of Informatics and Engineering, The University of Electro-Communications, 1-5-1 Chofugaoka, Chofu, Tokyo 182-8585, Japan

3. Graduate School of Engineering, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-8656, Japan

Abstract

Ultrasound (US) imaging is beneficial for kidney diagnosis; however, it involves sophisticated tasks that must be performed by physicians to obtain the target image. We propose a target-image search strategy combining visual servoing and deep learning-based image evaluation for robotic kidney US imaging. The search strategy is designed by mimicking physicians’ motion axis of the US probe. By controlling the position of the US probe along each of the motion axes while evaluating the obtained US images based on an anatomical feature extraction method via instance segmentation with YOLACT++, we are able to search for an optimal target image. The proposed approach was validated through phantom studies. The results showed that the proposed approach could find the target kidney images with error rates of 2.88±1.76 mm and 2.75±3.36°. Thus, the proposed method enables the accurate identification of the target image, which highlights its potential for application in autonomous kidney US imaging.

Funder

Japan Society for the Promotion of Science

Japan Science and Technology Agency

Publisher

Fuji Technology Press Ltd.

Subject

Electrical and Electronic Engineering,General Computer Science

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