Li Shiwei, Zhou Hang, Quan Tingwei, Li Jing, Li Yuxin, Li Anan, Luo Qingming, Gong Hui, Zeng Shaoqun
Britton Chance Center for Biomedical Photonics, Huazhong University of Science and Technology-Wuhan National Laboratory for Optoelectronics, Wuhan, 430074, China.
MoE Key Laboratory for Biomedical Photonics, Department of Biomedical Engineering, Huazhong University of Science and Technology, Wuhan, 430074, China.
Neuroinformatics. 2017 Apr;15(2):133-149. doi: 10.1007/s12021-016-9317-6.
Digital reconstruction of a single neuron occupies an important position in computational neuroscience. Although many novel methods have been proposed, recent advances in molecular labeling and imaging systems allow for the production of large and complicated neuronal datasets, which pose many challenges for neuron reconstruction, especially when discontinuous neuronal morphology appears in a strong noise environment. Here, we develop a new pipeline to address this challenge. Our pipeline is based on two methods, one is the region-to-region connection (RRC) method for detecting the initial part of a neurite, which can effectively gather local cues, i.e., avoid the whole image analysis, and thus boosts the efficacy of computation; the other is constrained principal curves method for completing the neurite reconstruction, which uses the past reconstruction information of a neurite for current reconstruction and thus can be suitable for tracing discontinuous neurites. We investigate the reconstruction performances of our pipeline and some of the best state-of-the-art algorithms on the experimental datasets, indicating the superiority of our method in reconstructing sparsely distributed neurons with discontinuous neuronal morphologies in noisy environment. We show the strong ability of our pipeline in dealing with the large-scale image dataset. We validate the effectiveness in dealing with various kinds of image stacks including those from the DIADEM challenge and BigNeuron project.
单个神经元的数字重建在计算神经科学中占据重要地位。尽管已经提出了许多新颖的方法，但分子标记和成像系统的最新进展使得能够生成大量复杂的神经元数据集，这给神经元重建带来了诸多挑战，尤其是当不连续的神经元形态出现在强噪声环境中时。在此，我们开发了一种新的流程来应对这一挑战。我们的流程基于两种方法，一种是用于检测神经突起始部分的区域到区域连接（RRC）方法，它能够有效地收集局部线索，即避免对整个图像进行分析，从而提高计算效率；另一种是用于完成神经突重建的约束主曲线方法，它利用神经突过去的重建信息进行当前重建，因此适用于追踪不连续的神经突。我们在实验数据集上研究了我们的流程以及一些最佳的现有算法的重建性能，表明我们的方法在重建噪声环境中具有不连续神经元形态的稀疏分布神经元方面具有优越性。我们展示了我们的流程处理大规模图像数据集的强大能力。我们验证了其在处理包括来自DIADEM挑战赛和BigNeuron项目的各种图像堆栈方面的有效性。
