[1]李玉慧,梁创学,李军.基于Group-Depth U-Net的电子显微图像中神经元结构分割[J].中国医学物理学杂志,2020,37(6):720-725.[doi:DOI:10.3969/j.issn.1005-202X.2020.06.012]
 LI Yuhui,LIANG Chuangxue,LI Jun.Segmentation of neural structure in electron microscopy image based on Group-Depth U-Net[J].Chinese Journal of Medical Physics,2020,37(6):720-725.[doi:DOI:10.3969/j.issn.1005-202X.2020.06.012]
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基于Group-Depth U-Net的电子显微图像中神经元结构分割()
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《中国医学物理学杂志》[ISSN:1005-202X/CN:44-1351/R]

卷:
37
期数:
2020年第6期
页码:
720-725
栏目:
医学影像物理
出版日期:
2020-06-25

文章信息/Info

Title:
Segmentation of neural structure in electron microscopy image based on Group-Depth U-Net
文章编号:
1005-202X(2020)06-0720-06
作者:
李玉慧梁创学李军
华南师范大学物理与电信工程学院, 广东 广州 510006
Author(s):
LI Yuhui LIANG Chuangxue LI Jun
School of Physics and Telecommunication Engineering, South China Normal University, Guangzhou 510006, China
关键词:
深层卷积神经网络分组卷积网络神经元结构分割电子显微成像Group-Depth U-Net
Keywords:
Keywords: deep convolutional neural network group convolutional network neural structure segmentation electron microscopy imaging Group-Depth U-Net
分类号:
R318;TP391
DOI:
DOI:10.3969/j.issn.1005-202X.2020.06.012
文献标志码:
A
摘要:
针对电子显微(EM)成像存在边界有损、模糊不均匀以及神经元结构本身轮廓纹理复杂难以定位的问题,提出一种深层卷积神经网络模型Group-Depth U-Net,以实现EM图像中神经元结构的自动分割。该模型采用更加深层的U-Net架构作为骨架网络,以获取更加丰富的图像特征信息;同时采用分组卷积网络结构,使模型更加高效、防止过拟合,从而提高分割的准确性与效率。公开的数据集实验表明该模型相比U-Net达到了更好的分割准确率。
Abstract:
Abstract: Aiming at the problems of electron microscopy (EM) imaging, such as boundary damage, fuzzy inhomogeneity and difficulty of localization due to the complex contour texture of neural structure itself, a deep convolutional neural network model, Group-Depth U-Net, is proposed to realize automatic segmentation of neural structure in EM image. In the proposed model, a deeper U-Net architecture is used as the backbone network to obtain more abundant image feature information. Meanwhile, group convolutional network structure is adopted to make the model more efficient and prevent over-fitting, thus improving the accuracy and efficiency of segmentation. The experiments on the open data set show that the proposed model achieves a higher segmentation accuracy than U-Net.

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[1]吴雪,王娆芬.基于迁移学习的深层卷积神经网络心电信号疲劳分类[J].中国医学物理学杂志,2021,38(10):1258.[doi:DOI:10.3969/j.issn.1005-202X.2021.10.013]
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备注/Memo

备注/Memo:
【收稿日期】2020-01-11 【基金项目】广东省自然科学基金(2015A030313384);广州市科技计划项目(201607010275) 【作者简介】李玉慧,硕士研究生,研究方向:图像处理、深度学习等,E-mail: 1320106222@qq.com 【通信作者】李军,副教授,研究方向:光学、图像处理、人工智能等,E-mail: lijunc@126.com
更新日期/Last Update: 2020-07-03