NEURD: automated proofreading and feature extraction for connectomics
| dc.contributor.advisor | Reimer, Jacob | |
| dc.contributor.advisor | Pitkow, Xaq | |
| dc.creator | Celii, Brendan | |
| dc.date.accessioned | 2024-05-21T21:06:26Z | |
| dc.date.available | 2024-05-21T21:06:26Z | |
| dc.date.created | 2024-05 | |
| dc.date.issued | 2024-03-28 | |
| dc.date.submitted | May 2024 | |
| dc.date.updated | 2024-05-21T21:06:26Z | |
| dc.description.abstract | We are now in the era of millimeter-scale electron microscopy (EM) volumes collected at nanometer resolution. Dense reconstruction of cellular compartments in these EM volumes has been enabled by recent advances in Machine Learning (ML). 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 tasks unable to be performed manually at these scales, such as 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. | |
| dc.format.mimetype | application/pdf | |
| dc.identifier.citation | Celii, Brendan. NEURD: automated proofreading and feature extraction for connectomics. (2024). PhD diss., Rice University. https://hdl.handle.net/1911/116093 | |
| dc.identifier.uri | https://hdl.handle.net/1911/116093 | |
| dc.language.iso | eng | |
| dc.rights | Copyright 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. | |
| dc.subject | functional connectomics | |
| dc.subject | machine learning | |
| dc.title | NEURD: automated proofreading and feature extraction for connectomics | |
| dc.type | Thesis | |
| dc.type.material | Text | |
| thesis.degree.department | Electrical and Computer Engineering | |
| thesis.degree.discipline | Engineering | |
| thesis.degree.grantor | Rice University | |
| thesis.degree.level | Doctoral | |
| thesis.degree.name | Doctor of Philosophy |
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