Ultrasonic Image Segmentation Method based on Improved Fully Convolution Network
Hai Ye, Kaiping Feng, Lihong Luo, Hongning Xie
Available Online April 2019.
- https://doi.org/10.2991/icmeit-19.2019.93How to use a DOI?
- Computer vision; Deep learning; Ultrasound image; Image segmentation.
- 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.
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 -