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 proceedingConference contribution


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 languageEnglish
Title of host publicationNew Trends in Computer Technologies and Applications - 23rd International Computer Symposium, ICS 2018, Revised Selected Papers
EditorsChuan-Yu Chang, Chien-Chou Lin, Horng-Horng Lin
PublisherSpringer Verlag
Number of pages7
ISBN (Print)9789811391897
Publication statusPublished - 2019
Event23rd International Computer Symposium, ICS 2018 - Yunlin, Taiwan
Duration: 2018 Dec 202018 Dec 22

Publication series

NameCommunications in Computer and Information Science
ISSN (Print)1865-0929
ISSN (Electronic)1865-0937


Conference23rd International Computer Symposium, ICS 2018

All Science Journal Classification (ASJC) codes

  • Computer Science(all)
  • Mathematics(all)


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