标题： 使用计算拓扑中的离散莫尔斯技术进行神经元过程的骨架化 显示英文标题

标题： Skeletonization of neuronal processes using Discrete Morse techniques from computational topology

摘要： 为了理解生物智能，我们需要绘制脊椎动物大脑中的神经网络。 通过注射示踪剂来映射中尺度神经回路，这些示踪剂标记投射到不同大脑区域的神经元群。 由于许多神经元被标记，很难追踪单个轴突。 先前的方法转而使用区域内总标记强度来量化区域投影。 然而，这种量化在生物学上没有意义。 我们提出了一种新的方法，通过将标记的轴突片段骨架化，然后估计体积长度密度，更好地连接到底层神经元。 我们的方法结合了深度网络和计算拓扑中的离散莫尔斯（DM）技术。 该技术考虑了非局部连通性信息，因此提供了抗噪性。 我们在整个大脑示踪剂注射数据上展示了这种方法的实用性和可扩展性。 我们还定义并说明了一个信息论度量，该度量量化了当获得单个轴突形态时，与骨架化的示踪剂注射片段相比所获得的额外信息。 我们的方法是DM技术在计算神经解剖学中的首次应用。 它可以帮助弥合单轴骨架和示踪剂注射之间的差距，这是绘制脊椎动物神经网络中的两种重要数据类型。 显示英文摘要

摘要： To understand biological intelligence we need to map neuronal networks in vertebrate brains. Mapping mesoscale neural circuitry is done using injections of tracers that label groups of neurons whose axons project to different brain regions. Since many neurons are labeled, it is difficult to follow individual axons. Previous approaches have instead quantified the regional projections using the total label intensity within a region. However, such a quantification is not biologically meaningful. We propose a new approach better connected to the underlying neurons by skeletonizing labeled axon fragments and then estimating a volumetric length density. Our approach uses a combination of deep nets and the Discrete Morse (DM) technique from computational topology. This technique takes into account nonlocal connectivity information and therefore provides noise-robustness. We demonstrate the utility and scalability of the approach on whole-brain tracer injected data. We also define and illustrate an information theoretic measure that quantifies the additional information obtained, compared to the skeletonized tracer injection fragments, when individual axon morphologies are available. Our approach is the first application of the DM technique to computational neuroanatomy. It can help bridge between single-axon skeletons and tracer injections, two important data types in mapping neural networks in vertebrates.