Knowledge Commons of Institute of Automation,CAS
An effective AI integrated system for neuron tracing on anisotropic electron microscopy volume | |
Liu, Jing1,2; Hong, Bei1,2; Chen, Xi1; Xie, Qiwei3; Tang, Yuanyan4; Han, Hua1,2,5 | |
发表期刊 | Biomedical Signal Processing and Control |
2021-08 | |
卷号 | 69期号:无页码:102829 |
摘要 | Electron microscopy has become the most important technique in the feld of connectomics. Several methods have been proposed in the literature to tackle the problem of dense reconstruction. However, sparse reconstruction, which is a promising technique, has not been extensively studied. As a result, we develop an AI integrated system for sparse reconstruction that can automatically trace neurons with only the initial seeded masks. First, as an important part of the system for interlayer information estimation, convolutional LSTMs are employed to estimate the spatial contexts between adjacent sections. Then, the intra-slice information is obtained by a lightweight U-Net. Moreover, we employ a novel recursive training method that can signifcantly improve the performance. To reduce the tracing errors caused by misalignments in large-scale data, we integrate a shift estimation and correction module that effectively improves the traced neuron length. To the best of our knowledge, this is the frst attempt to apply a recurrent neural network to the task of neuron tracing. In addition, our approach performs better than other state-of-the-art methods on two highly anisotropic datasets. |
关键词 | Neuron tracing Electron microscopy Deep learning Convolutional LSTM |
收录类别 | SCI |
语种 | 英语 |
文献类型 | 期刊论文 |
条目标识符 | http://ir.ia.ac.cn/handle/173211/46607 |
专题 | 脑图谱与类脑智能实验室_微观重建与智能分析 |
通讯作者 | Xie, Qiwei; Han, Hua |
作者单位 | 1.National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences 2.School of Artifcial Intelligence, School of Future Technology, University of Chinese Academy of Sciences 3.Research Base of Beijing Modern Manufacturing Development, Beijing University of Technology 4.University of Macau, Department of Computer and Information Science 5.CAS Center for Excellence in Brain Science and Intelligence Technology |
第一作者单位 | 模式识别国家重点实验室 |
通讯作者单位 | 模式识别国家重点实验室 |
推荐引用方式 GB/T 7714 | Liu, Jing,Hong, Bei,Chen, Xi,et al. An effective AI integrated system for neuron tracing on anisotropic electron microscopy volume[J]. Biomedical Signal Processing and Control,2021,69(无):102829. |
APA | Liu, Jing,Hong, Bei,Chen, Xi,Xie, Qiwei,Tang, Yuanyan,&Han, Hua.(2021).An effective AI integrated system for neuron tracing on anisotropic electron microscopy volume.Biomedical Signal Processing and Control,69(无),102829. |
MLA | Liu, Jing,et al."An effective AI integrated system for neuron tracing on anisotropic electron microscopy volume".Biomedical Signal Processing and Control 69.无(2021):102829. |
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