Understanding of neuronal circuitry at cellular resolution within the brain has relied on neuron tracing methods that involve careful observation and interpretation by experienced neuroscientists. With recent developments in imaging and digitization, this approach is no longer feasible with the large-scale (terabyte to petabyte range) images. Machine-learning-based techniques, using deep networks, provide an efficient alternative to the problem. However, these methods rely on very large volumes of annotated images for training and have error rates that are too high for scientific data analysis, and thus requires a substantial volume of human-in-the-loop proofreading. Here we introduce a hybrid architecture combining prior structure in the form of topological data analysis methods, based on discrete Morse theory, with the best-in-class deep-net architectures for the neuronal connectivity analysis. We show significant performance gains using our hybrid architecture on detection of topological structure (for example, connectivity of neuronal processes and local intensity maxima on axons corresponding to synaptic swellings) with precision and recall close to 90% compared with human observers. We have adapted our architecture to a high-performance pipeline capable of semantic segmentation of light-microscopic whole-brain image data into a hierarchy of neuronal compartments. We expect that the hybrid architecture incorporating discrete Morse techniques into deep nets will generalize to other data domains.

对大脑内细胞分辨率的神经元回路的理解依赖于神经元追踪方法，这些方法需要经验丰富的神经科学家的仔细观察和解释。随着成像和数字化的最新发展，这种方法对于大规模（TB 到 PB 范围）图像不再可行。使用深度网络的基于机器学习的技术为该问题提供了有效的替代方案。然而，这些方法依赖于大量带注释的图像进行训练，并且错误率对于科学数据分析来说太高，因此需要大量的人机交互校对。在这里，我们介绍了一种混合架构，它将基于离散莫尔斯理论的拓扑数据分析方法形式的先验结构与用于神经元连接分析的一流深度网络架构相结合。与人类观察者相比，我们使用混合架构检测拓扑结构（例如，神经元过程的连接性和对应于突触肿胀的轴突上的局部强度最大值）取得了显着的性能提升，精确度和召回率接近 90%。我们已经将我们的架构调整为高性能管道，能够将光显微全脑图像数据语义分割成神经元区室的层次结构。我们预计将离散莫尔斯技术融入深度网络的混合架构将推广到其他数据领域。