Proceedings of the 3rd International Conference on Mechatronics Engineering and Information Technology (ICMEIT 2019)

Ultrasonic Image Segmentation Method based on Improved Fully Convolution Network

Authors
Hai Ye, Kaiping Feng, Lihong Luo, Hongning Xie
Corresponding Author
Hai Ye
Available Online April 2019.
DOI
https://doi.org/10.2991/icmeit-19.2019.93How to use a DOI?
Keywords
Computer vision; Deep learning; Ultrasound image; Image segmentation.
Abstract
The clinical fetal brain ultrasound image contains a lot of noise, which is impact the classification or recognition results in deep learning tasks. Therefore, proposed an improved fully convolutional network which can automatically eliminate noise and extract effective area in fetal brain ultrasound images. By adding dilated convolution, U-net obtained a larger receptive field. The original U-net neural network was compared with the method by adding dilated convolution one. The experiment of test data shows that the improved segmentation model was effective and robust, and performed better in precise rate, recall rate, f1-score and DICE coefficient. This improved method can be wildly used in other medical image segmentation tasks.
Open Access
This is an open access article distributed under the CC BY-NC license.

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Proceedings
Part of series
Advances in Computer Science Research
Publication Date
April 2019
ISBN
978-94-6252-708-9
ISSN
2352-538X
DOI
https://doi.org/10.2991/icmeit-19.2019.93How to use a DOI?
Open Access
This is an open access article distributed under the CC BY-NC license.

Cite this article

TY  - CONF
AU  - Hai Ye
AU  - Kaiping Feng
AU  - Lihong Luo
AU  - Hongning Xie
PY  - 2019/04
DA  - 2019/04
TI  - Ultrasonic Image Segmentation Method based on Improved Fully Convolution Network
PB  - Atlantis Press
SP  - 581
EP  - 584
SN  - 2352-538X
UR  - https://doi.org/10.2991/icmeit-19.2019.93
DO  - https://doi.org/10.2991/icmeit-19.2019.93
ID  - Ye2019/04
ER  -