M-AMST: an automatic 3D neuron tracing method based on mean shift and adapted minimum spanning tree

BMC Bioinformatics

Methodology Article

Ning Zhong¹  Zhijiang Wan¹  Ming Hao²  Jian Yang²  Yishan He² 

[1] Beijing Advanced Innovation Center for Future Internet Technology, Beijing University of Technology, Beijing, China;Department of Life Science and Informatics, Maebashi Institute of Technology, Maebashi, Japan;International WIC Institute, Beijing University of Technology, Beijing, China;Beijing Key Laboratory of MRI and Brain Informatics, Beijing, China;Beijing International Collaboration Base on Brain Informatics and Wisdom Services, Beijing, China;Department of Life Science and Informatics, Maebashi Institute of Technology, Maebashi, Japan;International WIC Institute, Beijing University of Technology, Beijing, China;Beijing Key Laboratory of MRI and Brain Informatics, Beijing, China;Beijing International Collaboration Base on Brain Informatics and Wisdom Services, Beijing, China;

关键词: M-AMST;    Neuron reconstruction;    Mean shift;    Sphere model;    Coordinate transformation;   

DOI  :  10.1186/s12859-017-1597-9

 received in 2016-06-07, accepted in 2017-03-11,  发布年份 2017

来源: Springer

【 摘 要 】

BackgroundUnderstanding the working mechanism of the brain is one of the grandest challenges for modern science. Toward this end, the BigNeuron project was launched to gather a worldwide community to establish a big data resource and a set of the state-of-the-art of single neuron reconstruction algorithms. Many groups contributed their own algorithms for the project, including our mean shift and minimum spanning tree (M-MST). Although M-MST is intuitive and easy to implement, the MST just considers spatial information of single neuron and ignores the shape information, which might lead to less precise connections between some neuron segments. In this paper, we propose an improved algorithm, namely M-AMST, in which a rotating sphere model based on coordinate transformation is used to improve the weight calculation method in M-MST.ResultsTwo experiments are designed to illustrate the effect of adapted minimum spanning tree algorithm and the adoptability of M-AMST in reconstructing variety of neuron image datasets respectively. In the experiment 1, taking the reconstruction of APP2 as reference, we produce the four difference scores (entire structure average (ESA), different structure average (DSA), percentage of different structure (PDS) and max distance of neurons’ nodes (MDNN)) by comparing the neuron reconstruction of the APP2 and the other 5 competing algorithm. The result shows that M-AMST gets lower difference scores than M-MST in ESA, PDS and MDNN. Meanwhile, M-AMST is better than N-MST in ESA and MDNN. It indicates that utilizing the adapted minimum spanning tree algorithm which took the shape information of neuron into account can achieve better neuron reconstructions. In the experiment 2, 7 neuron image datasets are reconstructed and the four difference scores are calculated by comparing the gold standard reconstruction and the reconstructions produced by 6 competing algorithms. Comparing the four difference scores of M-AMST and the other 5 algorithm, we can conclude that M-AMST is able to achieve the best difference score in 3 datasets and get the second-best difference score in the other 2 datasets.ConclusionsWe develop a pathway extraction method using a rotating sphere model based on coordinate transformation to improve the weight calculation approach in MST. The experimental results show that M-AMST utilizes the adapted minimum spanning tree algorithm which takes the shape information of neuron into account can achieve better neuron reconstructions. Moreover, M-AMST is able to get good neuron reconstruction in variety of image datasets.

【 授权许可】

CC BY   
© The Author(s). 2017

【 图 表 】

Fig. 1

Fig. 1

Fig. 1

Fig. 6