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

研究成果: Conference contribution

摘要

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.

原文English
主出版物標題New Trends in Computer Technologies and Applications - 23rd International Computer Symposium, ICS 2018, Revised Selected Papers
編輯Chuan-Yu Chang, Chien-Chou Lin, Horng-Horng Lin
發行者Springer Verlag
頁面778-784
頁數7
ISBN(列印)9789811391897
DOIs
出版狀態Published - 2019
事件23rd International Computer Symposium, ICS 2018 - Yunlin, Taiwan
持續時間: 2018 十二月 202018 十二月 22

出版系列

名字Communications in Computer and Information Science
1013
ISSN(列印)1865-0929
ISSN(電子)1865-0937

Conference

Conference23rd International Computer Symposium, ICS 2018
國家Taiwan
城市Yunlin
期間18-12-2018-12-22

All Science Journal Classification (ASJC) codes

  • Computer Science(all)
  • Mathematics(all)

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