USG-Net: Deep Learning-based Ultrasound Scanning-Guide for an Orthopedic Sonographer

Kyungsu Lee; Jaeseung Yang; Moon Hwan Lee; Jin Ho Chang; Jun Young Kim; Jae Youn Hwang

doi:10.1007/978-3-031-16449-1_3

USG-Net: Deep Learning-based Ultrasound Scanning-Guide for an Orthopedic Sonographer

Kyungsu Lee, Jaeseung Yang, Moon Hwan Lee, Jin Ho Chang, Jun Young Kim, Jae Youn Hwang

Department of Electrical Engineering and Computer Science

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

2 Scopus citations

Abstract

Ultrasound (US) imaging is widely used in the field of medicine. US images containing pathological information are essential for better diagnosis. However, it is challenging to obtain informative US images because of their anatomical complexity, which is significantly dependent on the expertise of the sonographer. Therefore, in this study, we propose a fully automatic scanning-guide algorithm that assists unskilled sonographers in acquiring informative US images by providing accurate directions of probe movement to search for target disease regions. The main contributions of this study are: (1) proposing a new scanning-guide task that searches for a rotator cuff tear (RCT) region using a deep learning-based algorithm, i.e., ultrasound scanning-guide network (USG-Net); (2) constructing a dataset to optimize the corresponding deep learning algorithm. Multidimensional US images collected from 80 patients with RCT were processed to optimize the scanning-guide algorithm which classified the existence of RCT. Furthermore, the algorithm provides accurate directions for the RCT, if it is not in the current frame. The experimental results demonstrate that the fully optimized scanning-guide algorithm offers accurate directions to localize a probe within target regions and helps to acquire informative US images.

Original language	English
Title of host publication	Medical Image Computing and Computer Assisted Intervention – MICCAI 2022 - 25th International Conference, Proceedings
Editors	Linwei Wang, Qi Dou, P. Thomas Fletcher, Stefanie Speidel, Shuo Li
Publisher	Springer Science and Business Media Deutschland GmbH
Pages	23-32
Number of pages	10
ISBN (Print)	9783031164484
DOIs	https://doi.org/10.1007/978-3-031-16449-1_3
State	Published - 2022
Event	25th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2022 - Singapore, Singapore Duration: 18 Sep 2022 → 22 Sep 2022

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	13437 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	25th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2022
Country/Territory	Singapore
City	Singapore
Period	18/09/22 → 22/09/22

Bibliographical note

Publisher Copyright:
© 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Keywords

Deep learning
Probe navigation
Ultrasound imaging

Access to Document

10.1007/978-3-031-16449-1_3

Cite this

Lee, K., Yang, J., Lee, M. H., Chang, J. H., Kim, J. Y., & Hwang, J. Y. (2022). USG-Net: Deep Learning-based Ultrasound Scanning-Guide for an Orthopedic Sonographer. In L. Wang, Q. Dou, P. T. Fletcher, S. Speidel, & S. Li (Eds.), Medical Image Computing and Computer Assisted Intervention – MICCAI 2022 - 25th International Conference, Proceedings (pp. 23-32). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 13437 LNCS). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-3-031-16449-1_3

Lee, Kyungsu ; Yang, Jaeseung ; Lee, Moon Hwan et al. / USG-Net : Deep Learning-based Ultrasound Scanning-Guide for an Orthopedic Sonographer. Medical Image Computing and Computer Assisted Intervention – MICCAI 2022 - 25th International Conference, Proceedings. editor / Linwei Wang ; Qi Dou ; P. Thomas Fletcher ; Stefanie Speidel ; Shuo Li. Springer Science and Business Media Deutschland GmbH, 2022. pp. 23-32 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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title = "USG-Net: Deep Learning-based Ultrasound Scanning-Guide for an Orthopedic Sonographer",

abstract = "Ultrasound (US) imaging is widely used in the field of medicine. US images containing pathological information are essential for better diagnosis. However, it is challenging to obtain informative US images because of their anatomical complexity, which is significantly dependent on the expertise of the sonographer. Therefore, in this study, we propose a fully automatic scanning-guide algorithm that assists unskilled sonographers in acquiring informative US images by providing accurate directions of probe movement to search for target disease regions. The main contributions of this study are: (1) proposing a new scanning-guide task that searches for a rotator cuff tear (RCT) region using a deep learning-based algorithm, i.e., ultrasound scanning-guide network (USG-Net); (2) constructing a dataset to optimize the corresponding deep learning algorithm. Multidimensional US images collected from 80 patients with RCT were processed to optimize the scanning-guide algorithm which classified the existence of RCT. Furthermore, the algorithm provides accurate directions for the RCT, if it is not in the current frame. The experimental results demonstrate that the fully optimized scanning-guide algorithm offers accurate directions to localize a probe within target regions and helps to acquire informative US images.",

keywords = "Deep learning, Probe navigation, Ultrasound imaging",

author = "Kyungsu Lee and Jaeseung Yang and Lee, {Moon Hwan} and Chang, {Jin Ho} and Kim, {Jun Young} and Hwang, {Jae Youn}",

note = "Publisher Copyright: {\textcopyright} 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.; 25th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2022 ; Conference date: 18-09-2022 Through 22-09-2022",

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series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",

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Lee, K, Yang, J, Lee, MH, Chang, JH, Kim, JY & Hwang, JY 2022, USG-Net: Deep Learning-based Ultrasound Scanning-Guide for an Orthopedic Sonographer. in L Wang, Q Dou, PT Fletcher, S Speidel & S Li (eds), Medical Image Computing and Computer Assisted Intervention – MICCAI 2022 - 25th International Conference, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 13437 LNCS, Springer Science and Business Media Deutschland GmbH, pp. 23-32, 25th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2022, Singapore, Singapore, 18/09/22. https://doi.org/10.1007/978-3-031-16449-1_3

USG-Net: Deep Learning-based Ultrasound Scanning-Guide for an Orthopedic Sonographer. / Lee, Kyungsu; Yang, Jaeseung; Lee, Moon Hwan et al.
Medical Image Computing and Computer Assisted Intervention – MICCAI 2022 - 25th International Conference, Proceedings. ed. / Linwei Wang; Qi Dou; P. Thomas Fletcher; Stefanie Speidel; Shuo Li. Springer Science and Business Media Deutschland GmbH, 2022. p. 23-32 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 13437 LNCS).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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AU - Yang, Jaeseung

AU - Lee, Moon Hwan

AU - Chang, Jin Ho

AU - Kim, Jun Young

AU - Hwang, Jae Youn

PY - 2022

Y1 - 2022

N2 - Ultrasound (US) imaging is widely used in the field of medicine. US images containing pathological information are essential for better diagnosis. However, it is challenging to obtain informative US images because of their anatomical complexity, which is significantly dependent on the expertise of the sonographer. Therefore, in this study, we propose a fully automatic scanning-guide algorithm that assists unskilled sonographers in acquiring informative US images by providing accurate directions of probe movement to search for target disease regions. The main contributions of this study are: (1) proposing a new scanning-guide task that searches for a rotator cuff tear (RCT) region using a deep learning-based algorithm, i.e., ultrasound scanning-guide network (USG-Net); (2) constructing a dataset to optimize the corresponding deep learning algorithm. Multidimensional US images collected from 80 patients with RCT were processed to optimize the scanning-guide algorithm which classified the existence of RCT. Furthermore, the algorithm provides accurate directions for the RCT, if it is not in the current frame. The experimental results demonstrate that the fully optimized scanning-guide algorithm offers accurate directions to localize a probe within target regions and helps to acquire informative US images.

AB - Ultrasound (US) imaging is widely used in the field of medicine. US images containing pathological information are essential for better diagnosis. However, it is challenging to obtain informative US images because of their anatomical complexity, which is significantly dependent on the expertise of the sonographer. Therefore, in this study, we propose a fully automatic scanning-guide algorithm that assists unskilled sonographers in acquiring informative US images by providing accurate directions of probe movement to search for target disease regions. The main contributions of this study are: (1) proposing a new scanning-guide task that searches for a rotator cuff tear (RCT) region using a deep learning-based algorithm, i.e., ultrasound scanning-guide network (USG-Net); (2) constructing a dataset to optimize the corresponding deep learning algorithm. Multidimensional US images collected from 80 patients with RCT were processed to optimize the scanning-guide algorithm which classified the existence of RCT. Furthermore, the algorithm provides accurate directions for the RCT, if it is not in the current frame. The experimental results demonstrate that the fully optimized scanning-guide algorithm offers accurate directions to localize a probe within target regions and helps to acquire informative US images.

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Lee K, Yang J, Lee MH, Chang JH, Kim JY, Hwang JY. USG-Net: Deep Learning-based Ultrasound Scanning-Guide for an Orthopedic Sonographer. In Wang L, Dou Q, Fletcher PT, Speidel S, Li S, editors, Medical Image Computing and Computer Assisted Intervention – MICCAI 2022 - 25th International Conference, Proceedings. Springer Science and Business Media Deutschland GmbH. 2022. p. 23-32. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-031-16449-1_3