Wang Ching-Wei, Lee Yu-Ching, Pradana Hilmil, Zhou Zhi, Peng Hanchuan
Graduate Institute of Biomedical Engineering, National Taiwan University of Science and Technology, Taipei, Taiwan.
NTUST Center of Computer Vision and Medical Imaging, National Taiwan University of Science and Technology, Taipei, Taiwan.
Neuroinformatics. 2017 Apr;15(2):185-198. doi: 10.1007/s12021-017-9325-1.
Tracing of neuron paths is important in neuroscience. Recent studies have shown that it is possible to segment and reconstruct three-dimensional morphology of axons and dendrites using fully automatic neuron tracing methods. A specific tracer may be better than others for a specific dataset, but another tracer could perform better for some other datasets. Ensemble of learners is an effective way to improve learning accuracy in machine learning. We developed automatic ensemble neuron tracers, which consistently perform well on 57 datasets of 5 species collected from 7 laboratories worldwide. Quantitative evaluation based on the data generated by human annotators shows that the proposed ensemble tracers are valuable for 3D neuron tracing and can be widely applied to different datasets.
在神经科学中，追踪神经元路径非常重要。最近的研究表明，使用全自动神经元追踪方法可以分割和重建轴突和树突的三维形态。对于特定的数据集，一种特定的追踪器可能比其他追踪器更好，但另一种追踪器可能在其他一些数据集上表现得更好。在机器学习中，学习者集成是提高学习准确性的有效方法。我们开发了自动集成神经元追踪器，其在从全球7个实验室收集的5个物种的57个数据集上始终表现良好。基于人类注释者生成的数据进行的定量评估表明，所提出的集成追踪器对于三维神经元追踪很有价值，并且可以广泛应用于不同的数据集。
