周围神经MicroCT图像中神经束区域的自动分离

doi:10.3969/j.issn.0255-8297.2019.03.006

应用科学学报 ›› 2019, Vol. 37 ›› Issue (3): 359-368.doi: 10.3969/j.issn.0255-8297.2019.03.006

周围神经MicroCT图像中神经束区域的自动分离

李芳¹, 钟映春², 戚剑³, 罗鹏⁴

1. 广东财经大学信息学院, 广州 510300;
2. 广东工业大学自动化学院, 广州 510006;
3. 中山大学附属第一医院显微外科, 广州 510085;
4. 深圳市第六人民医院骨科, 深圳 518000

收稿日期:2018-03-07 修回日期:2018-07-22 出版日期:2019-05-31 发布日期:2019-05-31
作者简介:李芳,研究方向:机器学习、图像处理,E-mail:9696096@qq.com
基金资助:
广东省自然科学基金（No.2018A0303130137）；广东省高性能计算重点实验室开放课题项目基金（No.TH1528）；广东省哲学社会科学"十三·五"规划项目基金（No.GD17XGL20）资助

Automatic Extraction of Regions of Peripheral Nerve Internal Fascicular Groups from MicroCT Images

LI Fang¹, ZHONG Yingchun², QI Jian³, LUO Peng⁴

1. School of Information, Guangdong University of Finance & Economy, Guangzhou 510300, China;
2. School of Automation, Guangdong University of Technology, Guangzhou 510006, China;
3. Department of Plastic and Reconstructive Surgery, The First Affiliated Hospital of Sun Yat-sen University, Guangzhou 510080, China;
4. Department of Bone and Joint Surgery, Shenzhen Sixth People's Hospital, Shenzhen 518000, China

Received:2018-03-07 Revised:2018-07-22 Online:2019-05-31 Published:2019-05-31

摘要/Abstract

摘要： 针对经过饱和氯化钙染色的四肢周围神经MicroCT扫描图像，提出了一种自动分离神经束区域的方法.1）手工标注第1幅扫描图像中的神经束轮廓和非轮廓区域；2）采用稀疏自动编码机方法提取神经束轮廓和非轮廓的特征并训练神经网络，进而自动识别分离第2幅图像中神经束区域；3）将第2幅图像中的神经束轮廓作为标注样本去训练新的神经网络；4）不断循环该过程直至序列图像全部处理完成.实验结果表明，所提出的神经束轮廓自动分离方法可以达到手工分离精度的84.7%，处理完成522幅图像需要花费0.3~0.4 h.此外，该方法不仅能快速准确地分离出序列图像中的单根神经束，而且还能分离出分裂与合并阶段的神经束轮廓，具有比较强的适应性.

关键词: 周围神经, 神经束, 深度学习, 稀疏自动编码机, MicroCT

Abstract: A novel algorithm to extract the region of peripheral nerve internal fascicular groups automatically from MicroCT images is proposed. 1) the contour and non-contour of fascicular groups in the first MicroCT image were marked manually. 2) the sparse auto encoder (SAE) was employed to extract the features unsupervisedly and the corresponding neural network was trained in order to recognize the contour in the second image. 3) the contour and non-contour of fascicular groups in the second MicroCT image were used as the labeled samples to train a new neural network. 4) above process was repeated until all images were processed completely. The experimental results show that the accuracy of the algorithm in this paper reaches 84.7% of manual's and it can process 522 images only in 0.3~0.4 h. Additionally, not only the single groups but also the splitting and emerging groups can be extracted rapidly and accurately.

Key words: MicroCT, sparse auto encoder, peripheral nerve, fascicular groups, deep learning

中图分类号:

P751.1

李芳, 钟映春, 戚剑, 罗鹏. 周围神经MicroCT图像中神经束区域的自动分离[J]. 应用科学学报, 2019, 37(3): 359-368.

LI Fang, ZHONG Yingchun, QI Jian, LUO Peng. Automatic Extraction of Regions of Peripheral Nerve Internal Fascicular Groups from MicroCT Images[J]. Journal of Applied Sciences, 2019, 37(3): 359-368.

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周围神经MicroCT图像中神经束区域的自动分离

Automatic Extraction of Regions of Peripheral Nerve Internal Fascicular Groups from MicroCT Images

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