NEURD: automated proofreading and feature extraction for connectomics

Brendan Celii,Stelios Papadopoulos, Zhuokun Ding,Paul G. Fahey, Eric Wang,Christos Papadopoulos, Alexander B. Kunin,Saumil Patel, J. Alexander Bae,Agnes L. Bodor, Derrick Brittain, JoAnn Buchanan,Daniel J. Bumbarger, Manuel A. Castro,Erick Cobos, Sven Dorkenwald,Leila Elabbady, Akhilesh Halageri, Zhen Jia,Chris Jordan, Dan Kapner,Nico Kemnitz, Sam Kinn,Kisuk Lee, Kai Li,Ran Lu, Thomas Macrina,Gayathri Mahalingam, Eric Mitchell,Shanka Subhra Mondal,Shang Mu,Barak Nehoran,Sergiy Popovych, Casey M. Schneider-Mizell,William Silversmith, Marc Takeno,Russel Torres, Nicholas L. Turner,William Wong, Jingpeng Wu,Szi-chieh Yu, Wenjing Yin,Daniel Xenes, Lindsey M. Kitchell,Patricia K. Rivlin, Victoria A. Rose, Caitlyn A. Bishop,Brock Wester, Emmanouil Froudarakis,Edgar Y. Walker, Fabian Sinz,H. Sebastian Seung, Forrest Collman,Nuno Maçarico da Costa, R. Clay Reid,Xaq Pitkow, Andreas S. Tolias,Jacob Reimer

biorxiv(2024)

引用 68|浏览102
暂无评分
摘要
We are now in the era of millimeter-scale electron microscopy (EM) volumes collected at nanometer resolution ([Shapson-Coe et al., 2021][1]; [Consortium et al., 2021][2]). Dense reconstruction of cellular compartments in these EM volumes has been enabled by recent advances in Machine Learning (ML) ([Lee et al., 2017][3]; [Wu et al., 2021][4]; [Lu et al., 2021][5]; [Macrina et al., 2021][6]). Automated segmentation methods can now yield exceptionally accurate reconstructions of cells, but despite this accuracy, laborious post-hoc proofreading is still required to generate large connectomes free of merge and split errors. The elaborate 3-D meshes of neurons produced by these segmentations contain detailed morphological information, from the diameter, shape, and branching patterns of axons and dendrites, down to the fine-scale structure of dendritic spines. However, extracting information about these features can require substantial effort to piece together existing tools into custom workflows. Building on existing open-source software for mesh manipulation, here we present “NEURD”, a software package that decomposes each meshed neuron into a compact and extensively-annotated graph representation. With these feature-rich graphs, we implement workflows to automate a variety of tasks that would otherwise require extensive manual effort, such as state of the art automated post-hoc proofreading of merge errors, cell classification, spine detection, axon-dendritic proximities, and computation of other features. These features enable many downstream analyses of neural morphology and connectivity, making these new massive and complex datasets more accessible to neuroscience researchers focused on a variety of scientific questions. ### Competing Interest Statement XP is a co founder of Upload AI, LLC, a company in which he has financial interests. AST is co founder of Vathes Inc., and UploadAI LLC companies in which he has financial interests. JR is co founder of Vathes Inc., and UploadAI LLC companies in which he has financial interests. [1]: #ref-1 [2]: #ref-2 [3]: #ref-3 [4]: #ref-4 [5]: #ref-5 [6]: #ref-6
更多
查看译文
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络
Chat Paper
正在生成论文摘要