Accelerating connectomic proofreading for larger brains and multiple individuals

加速更大大脑和多个个体的连接组学校对

• 批准号: 10413515
• 负责人: Mala Murthy
• 金额: $ 214.39万
• 依托单位: PRINCETON UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-04-01 至 2025-03-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10413515
• 关键词: 3-Dimensional; Algorithms; Animal Model; Ants; Automation; Bees; Biological; Brain; Cell model; Collaborations; Communities; Computer software; Conflict (Psychology); Data; Data Set; Detection; Development; Drosophila genus; Electrons; Functional disorder; Human; Individual; Infrastructure; Institutes; Institution; International; Internet; Judgment; Manuals; Microscopic; Morphology; Mus; Mutation; Nervous system structure; Neurons; Noise; Readability; Resolution; Scientist; Skeleton; Software Engineering; Speed; Stereotyping; System; Technology; Translating; United States National Institutes of Health; Update; Visualization; Writing; automated segmentation; base; cell type; computer science; connectome; data structure; design; experience; fly; frontier; improved; innovation; large datasets; medical schools; microscopic imaging; neural circuit; novel; open source; prevent; reconstruction; software development; stereotypy; tool

Technology for automating the segmentation of neurons from electron microscopic (EM) data has improved dramatically, making it now possible to obtain accurate reconstructions of neural circuits from large EM volumes. However, even the best automation still must be followed by human proofreading to attain high accuracy. Hundreds of neuroscientists are already using our ChunkedGraph system for proofreading neural circuits. The system is fully web- and cloud-ready, facilitating seamless collaboration. The software is open-source, and at this writing is being operated by three institutions (Princeton, Harvard Medical School, Allen Institute) to serve the proofreading of four large datasets (fly and mouse) by international communities. A notable example is the FlyWire community, which at this writing is engaging over 160 scientists from 40 labs to proofread a whole Drosophila brain. The ChunkedGraph is on its way to becoming a standard and indispensable tool for connectomics. The data structure was designed to permit scaling to arbitrarily large datasets in principle, even to the whole mouse brain connectome project that is currently being considered by the NIH. In practice, there are deficiencies in the current implementation that impede efficiency of proofreading of datasets on the scale of the fly brain, and are preventing further scaling to larger volumes and brains. To remove these barriers to scaling, we will make it possible to upgrade a ChunkedGraph system after proofreading has already started, to take advantage of new and improved automated reconstructions made possible by advances in AI. We will make it possible to visualize neurons in 3D with multi-resolution sharded meshes and skeletons that are rapidly updated after every proofreading edit. We further propose to build a subsequent processing step that rapidly derives morphological features and skeletons, an important prerequisite for downstream analysis and scientific discovery. One of the next frontiers in connectomics is the reconstruction of multiple brains of the same species. For nervous systems with sufficient stereotypy, comparing reconstructions of different individuals can guide the detection and correction of errors. We will develop software that speeds up proofreading by automatically matching a reconstructed neuron to a reference reconstruction, and computing and suggesting candidate corrections if necessary. We will pilot this software for the Drosophila brain, for which multiple EM datasets are now appearing. The same software will be extendable to other model organisms with relatively stereotyped nervous systems (e.g. bee, ant, etc.). In the long term, the approach could further be extended to mammalian nervous systems once the field has developed sufficiently accurate morphological models of cell types. Our proofreading software will continue to be open source and freely accessible. Proofreading generates accurate wiring diagrams of neural circuits, which are helpful for understanding brain function and dysfunction.

用于从电子显微镜（EM）数据中自动分割神经元的技术已经改进 戏剧性的是，现在有可能从大型EM获得神经回路的精确重建， 卷然而，即使是最好的自动化仍然必须遵循人工校对，以达到高 精度数百名神经科学家已经在使用我们的ChunkedGraph系统来校对神经网络。 电路.该系统完全支持网络和云计算，促进了无缝协作。该软件 开源，在本文写作时，由三个机构（普林斯顿，哈佛医学院， 艾伦研究所）为国际社会校对四个大型数据集（苍蝇和老鼠）提供服务。一 值得注意的例子是FlyWire社区，在撰写本文时，该社区正在吸引来自40个实验室的160多名科学家， 校对了整个果蝇的大脑 ChunkedGraph正在成为连接组学的标准和不可或缺的工具。数据 结构的设计原则上允许扩展到任意大的数据集，甚至整个鼠标 脑连接体项目，目前正在考虑由美国国立卫生研究院。在实践中， 当前的实现妨碍了在果蝇大脑的规模上校对数据集的效率，以及 正在阻止进一步扩展到更大的体积和大脑。为了消除这些扩展障碍，我们将 可以在校对已经开始后升级ChunkedGraph系统，以利用新的 人工智能的进步使自动重建成为可能。我们将使之成为可能， 使用多分辨率分片网格和骨架在3D中可视化神经元， 每一次校对我们进一步建议建立一个后续的处理步骤， 形态特征和骨架，下游分析和科学研究的重要前提 的发现 连接组学的下一个前沿领域之一是重建同一物种的多个大脑。为 神经系统有足够的刻板印象，比较不同个体的重建可以指导 检测和纠正错误。我们将开发一种软件， 将重建的神经元与参考重建进行匹配， 必要时进行更正。我们将在果蝇大脑中试用该软件，其中多个EM数据集 现在出现。相同的软件将可扩展到具有相对刻板印象的其他模式生物 神经系统（如蜜蜂、蚂蚁等）。从长远来看，这种方法可以进一步扩展到哺乳动物 一旦该领域开发出足够精确的细胞类型形态学模型，神经系统的研究就可以开始了。 我们的校对软件将继续开源和免费访问。校对生成 神经回路的精确接线图，有助于理解大脑功能和功能障碍。