Automatic Finger Tendon Segmentation from Ultrasound Images Using Deep Learning

Chan Pang Kuok; Bo Siang Tsai; Tai Hua Yang; Fong Chin Su; I. Ming Jou; Yung Nien Sun

doi:10.1007/978-981-13-9190-3_84

Automatic Finger Tendon Segmentation from Ultrasound Images Using Deep Learning

Chan Pang Kuok, Bo Siang Tsai, Tai Hua Yang, Fong Chin Su, I. Ming Jou, Yung Nien Sun

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

Abstract

Ultrasound imaging is the most commonly applied method for the diagnosis and surgery of a trigger finger. However, the ultrasound images are noisy and the boundaries of tissues are usually very unclear and fuzzy. Therefore, an automatic computer assisted tool for the tissues segmentation is desired and developed. The segmentation results of the conventional methods were satisfactory but they usually depended on the prior knowledge. Recently, the deep-learning convolutional neural network (CNN) shows amazing performance on image processing and it can process the image end-to-end. In this study, we propose a finger tendon segmentation CNN which overcomes the requirement of prior knowledge and gives promising results on ultrasound images. The evaluation result is remarkable high with DSC 0.884 on 380 testing images and the prediction time is fast by 0.027 s per image. This work, to our best of knowledge, is the first deep learning finger tendon segmentation method from transverse ultrasound images.

Original language	English
Title of host publication	New Trends in Computer Technologies and Applications - 23rd International Computer Symposium, ICS 2018, Revised Selected Papers
Editors	Chuan-Yu Chang, Chien-Chou Lin, Horng-Horng Lin
Publisher	Springer Verlag
Pages	778-784
Number of pages	7
ISBN (Print)	9789811391897
DOIs	https://doi.org/10.1007/978-981-13-9190-3_84
Publication status	Published - 2019
Event	23rd International Computer Symposium, ICS 2018 - Yunlin, Taiwan Duration: 2018 Dec 20 → 2018 Dec 22

Publication series

Name	Communications in Computer and Information Science
Volume	1013
ISSN (Print)	1865-0929
ISSN (Electronic)	1865-0937

Conference

Conference	23rd International Computer Symposium, ICS 2018
Country/Territory	Taiwan
City	Yunlin
Period	18-12-20 → 18-12-22

All Science Journal Classification (ASJC) codes

Computer Science(all)
Mathematics(all)

Access to Document

10.1007/978-981-13-9190-3_84

Cite this

Kuok, C. P., Tsai, B. S., Yang, T. H., Su, F. C., Jou, I. M., & Sun, Y. N. (2019). Automatic Finger Tendon Segmentation from Ultrasound Images Using Deep Learning. In C-Y. Chang, C-C. Lin, & H-H. Lin (Eds.), New Trends in Computer Technologies and Applications - 23rd International Computer Symposium, ICS 2018, Revised Selected Papers (pp. 778-784). (Communications in Computer and Information Science; Vol. 1013). Springer Verlag. https://doi.org/10.1007/978-981-13-9190-3_84

Kuok, Chan Pang ; Tsai, Bo Siang ; Yang, Tai Hua et al. / Automatic Finger Tendon Segmentation from Ultrasound Images Using Deep Learning. New Trends in Computer Technologies and Applications - 23rd International Computer Symposium, ICS 2018, Revised Selected Papers. editor / Chuan-Yu Chang ; Chien-Chou Lin ; Horng-Horng Lin. Springer Verlag, 2019. pp. 778-784 (Communications in Computer and Information Science).

@inproceedings{76c54879106c4b8fba1e28a408404b66,

title = "Automatic Finger Tendon Segmentation from Ultrasound Images Using Deep Learning",

abstract = "Ultrasound imaging is the most commonly applied method for the diagnosis and surgery of a trigger finger. However, the ultrasound images are noisy and the boundaries of tissues are usually very unclear and fuzzy. Therefore, an automatic computer assisted tool for the tissues segmentation is desired and developed. The segmentation results of the conventional methods were satisfactory but they usually depended on the prior knowledge. Recently, the deep-learning convolutional neural network (CNN) shows amazing performance on image processing and it can process the image end-to-end. In this study, we propose a finger tendon segmentation CNN which overcomes the requirement of prior knowledge and gives promising results on ultrasound images. The evaluation result is remarkable high with DSC 0.884 on 380 testing images and the prediction time is fast by 0.027 s per image. This work, to our best of knowledge, is the first deep learning finger tendon segmentation method from transverse ultrasound images.",

author = "Kuok, {Chan Pang} and Tsai, {Bo Siang} and Yang, {Tai Hua} and Su, {Fong Chin} and Jou, {I. Ming} and Sun, {Yung Nien}",

note = "Funding Information: Acknowledgments. This work was supported by Ministry of Science and Technology, Taiwan under grant MOST 107-2634-F-006-005. It was carried out at the AI Biomedical Research Center, Tainan, Taiwan.; 23rd International Computer Symposium, ICS 2018 ; Conference date: 20-12-2018 Through 22-12-2018",

year = "2019",

doi = "10.1007/978-981-13-9190-3_84",

language = "English",

isbn = "9789811391897",

series = "Communications in Computer and Information Science",

publisher = "Springer Verlag",

pages = "778--784",

editor = "Chuan-Yu Chang and Chien-Chou Lin and Horng-Horng Lin",

booktitle = "New Trends in Computer Technologies and Applications - 23rd International Computer Symposium, ICS 2018, Revised Selected Papers",

address = "Germany",

}

Kuok, CP, Tsai, BS, Yang, TH , Su, FC, Jou, IM & Sun, YN 2019, Automatic Finger Tendon Segmentation from Ultrasound Images Using Deep Learning. in C-Y Chang, C-C Lin & H-H Lin (eds), New Trends in Computer Technologies and Applications - 23rd International Computer Symposium, ICS 2018, Revised Selected Papers. Communications in Computer and Information Science, vol. 1013, Springer Verlag, pp. 778-784, 23rd International Computer Symposium, ICS 2018, Yunlin, Taiwan, 18-12-20. https://doi.org/10.1007/978-981-13-9190-3_84

Automatic Finger Tendon Segmentation from Ultrasound Images Using Deep Learning. / Kuok, Chan Pang; Tsai, Bo Siang; Yang, Tai Hua et al.

New Trends in Computer Technologies and Applications - 23rd International Computer Symposium, ICS 2018, Revised Selected Papers. ed. / Chuan-Yu Chang; Chien-Chou Lin; Horng-Horng Lin. Springer Verlag, 2019. p. 778-784 (Communications in Computer and Information Science; Vol. 1013).

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

TY - GEN

T1 - Automatic Finger Tendon Segmentation from Ultrasound Images Using Deep Learning

AU - Kuok, Chan Pang

AU - Tsai, Bo Siang

AU - Yang, Tai Hua

AU - Su, Fong Chin

AU - Jou, I. Ming

AU - Sun, Yung Nien

N1 - Funding Information: Acknowledgments. This work was supported by Ministry of Science and Technology, Taiwan under grant MOST 107-2634-F-006-005. It was carried out at the AI Biomedical Research Center, Tainan, Taiwan.

PY - 2019

Y1 - 2019

N2 - Ultrasound imaging is the most commonly applied method for the diagnosis and surgery of a trigger finger. However, the ultrasound images are noisy and the boundaries of tissues are usually very unclear and fuzzy. Therefore, an automatic computer assisted tool for the tissues segmentation is desired and developed. The segmentation results of the conventional methods were satisfactory but they usually depended on the prior knowledge. Recently, the deep-learning convolutional neural network (CNN) shows amazing performance on image processing and it can process the image end-to-end. In this study, we propose a finger tendon segmentation CNN which overcomes the requirement of prior knowledge and gives promising results on ultrasound images. The evaluation result is remarkable high with DSC 0.884 on 380 testing images and the prediction time is fast by 0.027 s per image. This work, to our best of knowledge, is the first deep learning finger tendon segmentation method from transverse ultrasound images.

AB - Ultrasound imaging is the most commonly applied method for the diagnosis and surgery of a trigger finger. However, the ultrasound images are noisy and the boundaries of tissues are usually very unclear and fuzzy. Therefore, an automatic computer assisted tool for the tissues segmentation is desired and developed. The segmentation results of the conventional methods were satisfactory but they usually depended on the prior knowledge. Recently, the deep-learning convolutional neural network (CNN) shows amazing performance on image processing and it can process the image end-to-end. In this study, we propose a finger tendon segmentation CNN which overcomes the requirement of prior knowledge and gives promising results on ultrasound images. The evaluation result is remarkable high with DSC 0.884 on 380 testing images and the prediction time is fast by 0.027 s per image. This work, to our best of knowledge, is the first deep learning finger tendon segmentation method from transverse ultrasound images.

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DO - 10.1007/978-981-13-9190-3_84

M3 - Conference contribution

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SN - 9789811391897

T3 - Communications in Computer and Information Science

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BT - New Trends in Computer Technologies and Applications - 23rd International Computer Symposium, ICS 2018, Revised Selected Papers

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A2 - Lin, Chien-Chou

A2 - Lin, Horng-Horng

PB - Springer Verlag

T2 - 23rd International Computer Symposium, ICS 2018

Y2 - 20 December 2018 through 22 December 2018

ER -

Kuok CP, Tsai BS, Yang TH , Su FC, Jou IM, Sun YN. Automatic Finger Tendon Segmentation from Ultrasound Images Using Deep Learning. In Chang C-Y, Lin C-C, Lin H-H, editors, New Trends in Computer Technologies and Applications - 23rd International Computer Symposium, ICS 2018, Revised Selected Papers. Springer Verlag. 2019. p. 778-784. (Communications in Computer and Information Science). doi: 10.1007/978-981-13-9190-3_84

Automatic Finger Tendon Segmentation from Ultrasound Images Using Deep Learning

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