Frontiers in Neuroanatomy (Feb 2019)
Optimization of Traced Neuron Skeleton Using Lasso-Based Model
- Shiwei Li,
- Shiwei Li,
- Tingwei Quan,
- Tingwei Quan,
- Tingwei Quan,
- Cheng Xu,
- Cheng Xu,
- Qing Huang,
- Qing Huang,
- Hongtao Kang,
- Hongtao Kang,
- Yijun Chen,
- Yijun Chen,
- Anan Li,
- Anan Li,
- Ling Fu,
- Ling Fu,
- Qingming Luo,
- Qingming Luo,
- Hui Gong,
- Hui Gong,
- Shaoqun Zeng,
- Shaoqun Zeng
Affiliations
- Shiwei Li
- Britton Chance Center for Biomedical Photonics, Wuhan National Laboratory for Optoelectronics-Huazhong University of Science and Technology, Hubei, China
- Shiwei Li
- MoE Key Laboratory for Biomedical Photonics, Collaborative Innovation Center for Biomedical Engineering, School of Engineering Sciences, Huazhong University of Science and Technology, Hubei, China
- Tingwei Quan
- Britton Chance Center for Biomedical Photonics, Wuhan National Laboratory for Optoelectronics-Huazhong University of Science and Technology, Hubei, China
- Tingwei Quan
- MoE Key Laboratory for Biomedical Photonics, Collaborative Innovation Center for Biomedical Engineering, School of Engineering Sciences, Huazhong University of Science and Technology, Hubei, China
- Tingwei Quan
- School of Mathematics and Economics, Hubei University of Education, Hubei, China
- Cheng Xu
- Britton Chance Center for Biomedical Photonics, Wuhan National Laboratory for Optoelectronics-Huazhong University of Science and Technology, Hubei, China
- Cheng Xu
- MoE Key Laboratory for Biomedical Photonics, Collaborative Innovation Center for Biomedical Engineering, School of Engineering Sciences, Huazhong University of Science and Technology, Hubei, China
- Qing Huang
- Britton Chance Center for Biomedical Photonics, Wuhan National Laboratory for Optoelectronics-Huazhong University of Science and Technology, Hubei, China
- Qing Huang
- MoE Key Laboratory for Biomedical Photonics, Collaborative Innovation Center for Biomedical Engineering, School of Engineering Sciences, Huazhong University of Science and Technology, Hubei, China
- Hongtao Kang
- Britton Chance Center for Biomedical Photonics, Wuhan National Laboratory for Optoelectronics-Huazhong University of Science and Technology, Hubei, China
- Hongtao Kang
- MoE Key Laboratory for Biomedical Photonics, Collaborative Innovation Center for Biomedical Engineering, School of Engineering Sciences, Huazhong University of Science and Technology, Hubei, China
- Yijun Chen
- Britton Chance Center for Biomedical Photonics, Wuhan National Laboratory for Optoelectronics-Huazhong University of Science and Technology, Hubei, China
- Yijun Chen
- MoE Key Laboratory for Biomedical Photonics, Collaborative Innovation Center for Biomedical Engineering, School of Engineering Sciences, Huazhong University of Science and Technology, Hubei, China
- Anan Li
- Britton Chance Center for Biomedical Photonics, Wuhan National Laboratory for Optoelectronics-Huazhong University of Science and Technology, Hubei, China
- Anan Li
- MoE Key Laboratory for Biomedical Photonics, Collaborative Innovation Center for Biomedical Engineering, School of Engineering Sciences, Huazhong University of Science and Technology, Hubei, China
- Ling Fu
- Britton Chance Center for Biomedical Photonics, Wuhan National Laboratory for Optoelectronics-Huazhong University of Science and Technology, Hubei, China
- Ling Fu
- MoE Key Laboratory for Biomedical Photonics, Collaborative Innovation Center for Biomedical Engineering, School of Engineering Sciences, Huazhong University of Science and Technology, Hubei, China
- Qingming Luo
- Britton Chance Center for Biomedical Photonics, Wuhan National Laboratory for Optoelectronics-Huazhong University of Science and Technology, Hubei, China
- Qingming Luo
- MoE Key Laboratory for Biomedical Photonics, Collaborative Innovation Center for Biomedical Engineering, School of Engineering Sciences, Huazhong University of Science and Technology, Hubei, China
- Hui Gong
- Britton Chance Center for Biomedical Photonics, Wuhan National Laboratory for Optoelectronics-Huazhong University of Science and Technology, Hubei, China
- Hui Gong
- MoE Key Laboratory for Biomedical Photonics, Collaborative Innovation Center for Biomedical Engineering, School of Engineering Sciences, Huazhong University of Science and Technology, Hubei, China
- Shaoqun Zeng
- Britton Chance Center for Biomedical Photonics, Wuhan National Laboratory for Optoelectronics-Huazhong University of Science and Technology, Hubei, China
- Shaoqun Zeng
- MoE Key Laboratory for Biomedical Photonics, Collaborative Innovation Center for Biomedical Engineering, School of Engineering Sciences, Huazhong University of Science and Technology, Hubei, China
- DOI
- https://doi.org/10.3389/fnana.2019.00018
- Journal volume & issue
-
Vol. 13
Abstract
Reconstruction of neuronal morphology from images involves mainly the extraction of neuronal skeleton points. It is an indispensable step in the quantitative analysis of neurons. Due to the complex morphology of neurons, many widely used tracing methods have difficulties in accurately acquiring skeleton points near branch points or in structures with tortuosity. Here, we propose two models to solve these problems. One is based on an L1-norm minimization model, which can better identify tortuous structure, namely, a local structure with large curvature skeleton points; the other detects an optimized branch point by considering the combination patterns of all neurites that link to this point. We combined these two models to achieve optimized skeleton detection for a neuron. We validate our models in various datasets including MOST and BigNeuron. In addition, we demonstrate that our method can optimize the traced skeletons from large-scale images. These characteristics of our approach indicate that it can reduce manual editing of traced skeletons and help to accelerate the accurate reconstruction of neuronal morphology.
Keywords
- Lasso-based model
- neuronal morphology reconstruction
- neuronal image
- model optimization
- branch points
