超声图像神经分割方法研究

doi:10.13190/j.jbupt.2017-151

北京邮电大学学报 ›› 2018, Vol. 41 ›› Issue (1): 115-120.doi: 10.13190/j.jbupt.2017-151

超声图像神经分割方法研究

徐晨阳, 李梦昕, 杨娟

北京邮电大学通信软件工程中心, 北京 100876

收稿日期:2017-07-20 出版日期:2018-02-28 发布日期:2018-01-04
作者简介:徐晨阳(1993-),男,硕士生,E-mail:xcy_haha@163.com;杨娟(1972-),女,副教授,硕士导师.

Neural Segmentation Method of Ultrasound Image

XU Chen-yang, LI Meng-xin, YANG Juan

Communication Software Engineering Center, Beijing University of Posts and Telecommunications, Beijing 100876, China

Received:2017-07-20 Online:2018-02-28 Published:2018-01-04

摘要/Abstract

摘要： 为了提升超声图像中的神经分割效果，提出了一种新的网络结构残差U型网络.相比于现有的U-net网络，残差U型网络加深了网络结构，提高了网络的表达能力；通过对每层参数进行规范化处理，减少了训练时间，提高了神经分割效果.实验结果表明，残差U型网络在分割效果比U-net网络提升了约13%，比SegNet网络提升了约7%.

关键词: 深度学习, 神经分割, 卷积神经网络

Abstract: To improve the efficiency of neural segmentation in ultrasound images, we propose a new neural structure the U-shape residual network. Compared with U-net network, this structure deepens the original structure to improve the expression ability. By standardizing the parameters of each layer, the structure reduces the training time and improve the segmentation effect. According to the results, the U-shape residual network segmentation effect increased by 13% compared with U-net network and improved about 7% compared with SegNet network.

Key words: deep learning, neural segmentation, convolutional neural network

中图分类号:

TP18

徐晨阳, 李梦昕, 杨娟. 超声图像神经分割方法研究[J]. 北京邮电大学学报, 2018, 41(1): 115-120.

XU Chen-yang, LI Meng-xin, YANG Juan. Neural Segmentation Method of Ultrasound Image[J]. JOURNAL OF BEIJING UNIVERSITY OF POSTS AND TELECOM, 2018, 41(1): 115-120.

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超声图像神经分割方法研究

Neural Segmentation Method of Ultrasound Image

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