NEURD: automated proofreading and feature extraction for connectomics

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dc.contributor.advisorPitkow, Xaqen_US
dc.contributor.advisorReimer, Jacoben_US
dc.creatorCelii, Brendanen_US
dc.date.accessioned2023-08-09T18:56:10Zen_US
dc.date.available2023-08-09T18:56:10Zen_US
dc.date.created2023-05en_US
dc.date.issued2023-04-21en_US
dc.date.submittedMay 2023en_US
dc.date.updated2023-08-09T18:56:10Zen_US
dc.description.abstractWe are now in the era of millimeter-scale electron microscopy (EM) volumes collected at nanometer resolution (Shapson-Coe et al., 2021; Consortium et al., 2021). Dense reconstruction of cellular compartments in these EM volumes has been enabled by recent advances in Machine Learning (ML) (Lee et al., 2017; Wu et al., 2021; Lu et al., 2021; Macrina et al., 2021). 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 for state of the art automated post-hoc proofreading of merge errors, cell classification, spine detection, axon-dendritic proximities, and other features that can enable many downstream analyses of neural morphology and connectivity. NEURD can make these new massive and complex datasets more accessible to neuroscience researchers focused on a variety of scientific questions.en_US
dc.format.mimetypeapplication/pdfen_US
dc.identifier.citationCelii, Brendan. "NEURD: automated proofreading and feature extraction for connectomics." (2023) Master’s Thesis, Rice University. <a href="https://hdl.handle.net/1911/115158">https://hdl.handle.net/1911/115158</a>.en_US
dc.identifier.urihttps://hdl.handle.net/1911/115158en_US
dc.language.isoengen_US
dc.rightsCopyright is held by the author, unless otherwise indicated. Permission to reuse, publish, or reproduce the work beyond the bounds of fair use or other exemptions to copyright law must be obtained from the copyright holder.en_US
dc.subjectEM connectomicsen_US
dc.subjectNeural Morphologyen_US
dc.subjectAutomated Proofreadingen_US
dc.subjectNeural Annotationen_US
dc.titleNEURD: automated proofreading and feature extraction for connectomicsen_US
dc.typeThesisen_US
dc.type.materialTexten_US
thesis.degree.departmentElectrical and Computer Engineeringen_US
thesis.degree.disciplineEngineeringen_US
thesis.degree.grantorRice Universityen_US
thesis.degree.levelMastersen_US
thesis.degree.nameMaster of Scienceen_US
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