NeuroFly：全脑单神经元重建框架
NeuroFly: A framework for whole-brain single neuron reconstruction
AI总结：
NeuroFly提出一种三阶段（分割、连接、校对）的全脑单神经元自动重建框架，通过计算机视觉基线模型处理密集分支等挑战，提升重建效率并开源数据与工具。
摘要：
神经元具有细长的树状树突和轴突结构，能够实现高效的信号整合和跨脑区的长距离通信。通过重建单个神经元的形态，我们可以获得对脑连接组学的宝贵见解，揭示认知、运动和感知的结构基础。尽管积累了大量的三维显微成像数据，但由于缺乏自动化工具来简化这一过程，进展受到了相当大的阻碍。在此，我们介绍NeuroFly，一个经过验证的大规模自动单神经元重建框架。该框架将过程分为三个不同阶段：分割、连接和校对。在分割阶段，我们执行自动分割，随后进行骨架化，以生成无分支的过分割神经元片段。在连接阶段，我们使用基于三维图像的路径跟踪方法，扩展每个片段并将其与同一神经元的其他片段连接起来。最后，人类标注者只需校对少数未解决的位置。我们过程的前两个阶段是明确定义的计算机视觉问题，我们已经训练了稳健的基线模型来解决它们。我们使用内部数据集验证了NeuroFly的效率，这些数据集包含各种具有挑战性的场景，如密集的树状结构、弱轴突和受污染的图像。我们将发布数据集以及一套可视化和标注工具，以提高可重复性。我们的目标是促进研究人员之间的合作，以应对神经元重建挑战，最终加速神经科学研究的进展。数据集和代码可在https://github.com/beanli161514/neurofly获取。
英文摘要：
Neurons, with their elongated, tree-like dendritic and axonal structures, enable efficient signal integration and long-range communication across brain regions. By reconstructing individual neurons' morphology, we can gain valuable insights into brain connectivity, revealing the structure basis of cognition, movement, and perception. Despite the accumulation of extensive 3D microscopic imaging data, progress has been considerably hindered by the absence of automated tools to streamline this process. Here we introduce NeuroFly, a validated framework for large-scale automatic single neuron reconstruction. This framework breaks down the process into three distinct stages: segmentation, connection, and proofreading. In the segmentation stage, we perform automatic segmentation followed by skeletonization to generate over-segmented neuronal fragments without branches. During the connection stage, we use a 3D image-based path following approach to extend each fragment and connect it with other fragments of the same neuron. Finally, human annotators are required only to proofread the few unresolved positions. The first two stages of our process are clearly defined computer vision problems, and we have trained robust baseline models to solve them. We validated NeuroFly's efficiency using in-house datasets that include a variety of challenging scenarios, such as dense arborizations, weak axons, images with contamination. We will release the datasets along with a suite of visualization and annotation tools for better reproducibility. Our goal is to foster collaboration among researchers to address the neuron reconstruction challenge, ultimately accelerating advancements in neuroscience research. The dataset and code are available at https://github.com/beanli161514/neurofly
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
