SmartTracing: self-learning-based Neuron reconstruction

被引:1
作者
Chen H. [1 ,2 ]
Xiao H. [3 ]
Liu T. [2 ]
Peng H. [1 ]
机构
[1] Allen Institute for Brain Science, Seattle, WA
[2] Cortical Architecture Imaging and Discovery Lab, Department of Computer Science and Bioimaging Research Center, The University of Georgia, Athens, GA
[3] CAS-MPG Partner Institute for Computational Biology, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, 320 Yueyang Road, Shanghai
关键词
Machine learning; Neuron morphology; Neuron reconstruction; Reconstruction confidence; SmartTracing;
D O I
10.1007/s40708-015-0018-y
中图分类号
学科分类号
摘要
In this work, we propose SmartTracing, an automatic tracing framework that does not require substantial human intervention. There are two major novelties in SmartTracing. First, given an input image, SmartTracing invokes a user-provided existing neuron tracing method to produce an initial neuron reconstruction, from which the likelihood of every neuron reconstruction unit is estimated. This likelihood serves as a confidence score to identify reliable regions in a neuron reconstruction. With this score, SmartTracing automatically identifies reliable portions of a neuron reconstruction generated by some existing neuron tracing algorithms, without human intervention. These reliable regions are used as training exemplars. Second, from the training exemplars the most characteristic wavelet features are automatically selected and used in a machine learning framework to predict all image areas that most probably contain neuron signal. Since the training samples and their most characterizing features are selected from each individual image, the whole process is automatically adaptive to different images. Notably, SmartTracing can improve the performance of an existing automatic tracing method. In our experiment, with SmartTracing we have successfully reconstructed complete neuron morphology of 120 Drosophila neurons. In the future, the performance of SmartTracing will be tested in the BigNeuron project (bigneuron.org). It may lead to more advanced tracing algorithms and increase the throughput of neuron morphology-related studies. © 2015, The Author(s).
引用
收藏
页码:135 / 144
页数:9
相关论文
共 22 条
[1]  
Donohue D.E., Ascoli G.A., Automated reconstruction of neuronal morphology: an overview, Brain Res Rev, 67, pp. 94-102, (2011)
[2]  
Parekh R., Ascoli G.A., Neuronal morphology goes digital: a research hub for cellular and system neuroscience, Neuron, 77, pp. 1017-1038, (2013)
[3]  
Meijering E., Neuron tracing in perspective, Cytometry A, 77, pp. 693-704, (2010)
[4]  
Xiao H., Peng H., APP2: automatic tracing of 3D neuron morphology based on hierarchical pruning of a gray-weighted image distance-tree, Bioinformatics, 29, pp. 1448-1454, (2013)
[5]  
Peng H., Long F., Myers G., Automatic 3D neuron tracing using all-path pruning, Bioinformatics, 27, pp. i239-i247, (2011)
[6]  
Lee P.-C., Chuang C.-C., Chiang A.-S., Ching Y.-T., High-throughput computer method for 3D neuronal structure reconstruction from the image stack of the Drosophila brain and its applications, PLoS Comput Biol, 8, (2012)
[7]  
Yang J., Gonzalez-Bellido P.T., Peng H., A distance-field based automatic neuron tracing method, BMC Bioinform, 14, (2013)
[8]  
Wang Y., Narayanaswamy A., Tsai C.-L., Roysam B., A broadly applicable 3-D neuron tracing method based on open-curve snake, Neuroinformatics, 9, pp. 193-217, (2011)
[9]  
Peng H., Ruan Z., Atasoy D., Sternson S., Automatic reconstruction of 3D neuron structures using a graph-augmented deformable model, Bioinformatics, 26, pp. i38-i46, (2010)
[10]  
Peng H., Meijering E., Ascoli G.A., From DIADEM to BigNeuron, Neuroinformatics, 13, pp. 259-260, (2015)