Luo Gongning, Sui Dong, Wang Kuanquan, Chae Jinseok
Research Center of Perception and Computing, School of Computer Science and Technology, Harbin Institute of Technology, Harbin, China.
Department of Computer Science and Engineering, Incheon National University, Incheon, Korea.
BMC Bioinformatics. 2015 Oct 24;16:342. doi: 10.1186/s12859-015-0780-0.
Reconstruction of neuron anatomy structure is a challenging and important task in neuroscience. However, few algorithms can automatically reconstruct the full structure well without manual assistance, making it essential to develop new methods for this task.
This paper introduces a new pipeline for reconstructing neuron anatomy structure from 3-D microscopy image stacks. This pipeline is initialized with a set of seeds that were detected by our proposed Sliding Volume Filter (SVF), given a non-circular cross-section of a neuron cell. Then, an improved open curve snake model combined with a SVF external force is applied to trace the full skeleton of the neuron cell. A radius estimation method based on a 2D sliding band filter is developed to fit the real edge of the cross-section of the neuron cell. Finally, a surface reconstruction method based on non-parallel curve networks is used to generate the neuron cell surface to finish this pipeline.
The proposed pipeline has been evaluated using publicly available datasets. The results show that the proposed method achieves promising results in some datasets from the DIgital reconstruction of Axonal and DEndritic Morphology (DIADEM) challenge and new BigNeuron project.
The new pipeline works well in neuron tracing and reconstruction. It can achieve higher efficiency, stability and robustness in neuron skeleton tracing. Furthermore, the proposed radius estimation method and applied surface reconstruction method can obtain more accurate neuron anatomy structures.
神经元解剖结构的重建是神经科学中一项具有挑战性且重要的任务。然而，几乎没有算法能够在没有人工辅助的情况下自动很好地重建完整结构，因此开发针对此任务的新方法至关重要。
本文介绍了一种从三维显微镜图像堆栈重建神经元解剖结构的新流程。给定神经元细胞的非圆形横截面，该流程以通过我们提出的滑动体积滤波器（SVF）检测到的一组种子点进行初始化。然后，应用一种改进的开放曲线蛇模型并结合SVF外力来追踪神经元细胞的完整骨架。开发了一种基于二维滑动带滤波器的半径估计方法，以拟合神经元细胞横截面的真实边缘。最后，使用基于非平行曲线网络的表面重建方法生成神经元细胞表面，从而完成该流程。
已使用公开可用的数据集对所提出的流程进行了评估。结果表明，在轴突和树突形态的数字重建（DIADEM）挑战和新的大神经元项目的一些数据集中，所提出的方法取得了有前景的结果。
新流程在神经元追踪和重建方面效果良好。它在神经元骨架追踪中可以实现更高的效率、稳定性和鲁棒性。此外，所提出的半径估计方法和应用的表面重建方法能够获得更准确的神经元解剖结构。
