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
基本信息
- 批准号:10413515
- 负责人:
- 金额:$ 214.39万
- 依托单位:
- 依托单位国家:美国
- 项目类别:
- 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:
- 关键词:3-DimensionalAlgorithmsAnimal ModelAntsAutomationBeesBiologicalBrainCell modelCollaborationsCommunitiesComputer softwareConflict (Psychology)DataData SetDetectionDevelopmentDrosophila genusElectronsFunctional disorderHumanIndividualInfrastructureInstitutesInstitutionInternationalInternetJudgmentManualsMicroscopicMorphologyMusMutationNervous system structureNeuronsNoiseReadabilityResolutionScientistSkeletonSoftware EngineeringSpeedStereotypingSystemTechnologyTranslatingUnited States National Institutes of HealthUpdateVisualizationWritingautomated segmentationbasecell typecomputer scienceconnectomedata structuredesignexperienceflyfrontierimprovedinnovationlarge datasetsmedical schoolsmicroscopic imagingneural circuitnovelopen sourcepreventreconstructionsoftware developmentstereotypytool
项目摘要
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.
从电子显微镜(EM)数据中自动分割神经元的技术已得到改进
戏剧性地,使现在有可能从大型EM获得准确的神经电路重建
音量。然而,即使是最好的自动化,也必须遵循人工校对才能达到最高
精确度。数以百计的神经学家已经在使用我们的ChunkedGraph系统来校对神经
电路。该系统完全支持Web和云,有助于实现无缝协作。该软件是
开源,在撰写本文时,由三个机构(普林斯顿、哈佛医学院、
艾伦研究所)为国际社会对四个大型数据集(苍蝇和老鼠)的校对提供服务。一个
值得注意的例子是flywire社区,在撰写本文时,该社区吸引了来自40个实验室的160多名科学家
校对整个果蝇的大脑。
ChunkedGraph正在成为连接分析的标准和不可或缺的工具。数据
结构原则上允许扩展到任意大的数据集,甚至扩展到整个鼠标
NIH目前正在考虑的大脑连接体项目。在实践中,有一些不足之处
当前的实施方式阻碍了苍蝇脑规模的数据集的校对效率,以及
阻止了进一步扩展到更大的体积和大脑。为了消除这些扩展障碍,我们将使
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。我们将使其成为可能
使用多分辨率分片网格和骨架在3D中可视化神经元,这些网格和骨架在
每一次校对编辑。我们还建议构建一个后续处理步骤,该步骤可以快速派生出
形态特征和骨骼,是下游分析和科学研究的重要前提
发现号。
连接学的下一个前沿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
错误的检测和纠正。我们将开发软件,通过自动校对来加快校对速度
将重建的神经元与参考重建进行匹配,并计算和建议候选
如有必要,请更正。我们将在果蝇的大脑中试用这个软件,多个EM数据集是
现在出现了。同样的软件将可以扩展到其他相对刻板的模式生物
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身上。
神经系统一旦该领域发展出足够精确的细胞类型形态模型。
我们的校对软件将继续是开放源码的,并可免费获取。校对产生
准确的神经电路图,有助于了解大脑的功能和功能障碍。
项目成果
期刊论文数量(0)
专著数量(0)
科研奖励数量(0)
会议论文数量(0)
专利数量(0)
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Mala Murthy其他文献
Mala Murthy的其他文献
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{{ truncateString('Mala Murthy', 18)}}的其他基金
Dissemination of FlyWire, A Whole-Brain Connectomics Resource
全脑连接组学资源 FlyWire 的传播
- 批准号:
10439970 - 财政年份:2022
- 资助金额:
$ 214.39万 - 项目类别:
Dissemination of FlyWire, A Whole-Brain Connectomics Resource
全脑连接组学资源 FlyWire 的传播
- 批准号:
10668452 - 财政年份:2022
- 资助金额:
$ 214.39万 - 项目类别:
Uncovering the Neural Mechanisms that Flexibly Link Sensory Processing to Behavior
揭示将感觉处理与行为灵活联系起来的神经机制
- 批准号:
10396643 - 财政年份:2019
- 资助金额:
$ 214.39万 - 项目类别:
Uncovering the Neural Mechanisms that Flexibly Link Sensory Processing to Behavior
揭示将感觉处理与行为灵活联系起来的神经机制
- 批准号:
9924657 - 财政年份:2019
- 资助金额:
$ 214.39万 - 项目类别:
Uncovering the Neural Mechanisms that Flexibly Link Sensory Processing to Behavior
揭示将感觉处理与行为灵活联系起来的神经机制
- 批准号:
10630079 - 财政年份:2019
- 资助金额:
$ 214.39万 - 项目类别:
How does the brain solve the pattern recognition problem?
大脑如何解决模式识别问题?
- 批准号:
8755764 - 财政年份:2014
- 资助金额:
$ 214.39万 - 项目类别:
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