DVID: Distributed Versioned Image-Oriented Dataservice

Katz, William T.; Plaza, Stephen M.

doi:10.3389/fncir.2019.00005

Cited by 31 publications

(37 citation statements)

References 23 publications

(32 reference statements)

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“…Furthermore, the backend of the software layer abstracts the storage into different technologyspecific plugins. For the non-graph data, plugins exist to access DVID [10] and a generic key-value database. Other databases that can satisfy the interface requirements can be easily added, such as Google storage or Amazon S3.…”

Section: Interfacing With Neuprintmentioning

confidence: 99%

“…We have light-weight services written in Python that listen for changes recorded to this log and modify the neuPrint data model using targeted Cypher statements. For example, a user can modify segmentation data using a tool like [14], which modifies data managed by DVID [10]. DVID then emits log messages to Kafka, which our Python services then consumes and updates neuPrint graph data through neuPrintHTTP.…”

Section: Incrementally Updating the Neuprint Graph Data Modelmentioning

confidence: 99%

“…Our web interface combines 3D visualization and a flexible plugin system to enable the rapid creation of new analysis tools to meet the demands of new usage patterns for this emerging field. Furthermore, the neuprint ecosystem can leverage other storage solutions, e.g., [10], for non-graph connectome-relevant data, such as morphological skeletons, useful in tasks such as delay modeling.…”

Section: Introductionmentioning

confidence: 99%

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neuPrint: Analysis Tools for EM Connectomics

Clements

Dolafi

Umayam

et al. 2020

Preprint

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144

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Due to technological advances in electron microscopy (EM) and deep learning, it is now practical to reconstruct a connectome, a description of neurons and the connections between them, for significant volumes of neural tissue. The limited scope of past reconstructions meant they were primarily used by domain experts, and performance was not a serious problem. But the new reconstructions, of common laboratory creatures such as the fruit fly Drosophila melanogaster, upend these assumptions. These natural neural networks now contain tens of thousands of neurons and tens of millions of connections between them, with yet larger reconstructions pending, and are of interest to a large community of non-specialists. This requires new tools that are easy to use and efficiently handle large data. We introduce neuPrint to address these data analysis challenges. neuPrint is a database and analysis ecosystem that organizes connectome data in a manner conducive to biological discovery. In particular, we propose a data model that allows users to access the connectome at different levels of abstraction primarily through a graph database, neo4j, and its powerfully expressive query language Cypher. neuPrint is compatible with modern connectome reconstruction workflows, providing tools for assessing reconstruction quality, and offering both batch and incremental updates to match modern connectome reconstruction flows. Finally, we introduce a web interface and programmer API that targets a diverse user skill set. We demonstrate the effectiveness and efficiency of neuPrint through example database queries.

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Section: Interfacing With Neuprintmentioning

confidence: 99%

Section: Incrementally Updating the Neuprint Graph Data Modelmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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neuPrint: Analysis Tools for EM Connectomics

Clements

Dolafi

Umayam

et al. 2020

Preprint

Self Cite

144

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“…Data like supervoxel meshes and associations between supervoxels and agglomerated bodies are stored in the DVID system [9]. DVID supports branched versioning to maintain data provenance during proofreading, and provides a REST API that translates high-level operations like cleaving and merging to low-level updates to an underlying key-value store.…”

Section: Proofreading Overviewmentioning

confidence: 99%

“…A fast graph-cut re-segmentation algorithm for cleaving a segment along its supervoxel boundaries, implemented in conjunction with DVID [9], a web service that supports data versioning.…”

mentioning

confidence: 99%

Accelerated EM Connectome Reconstruction using 3D Visualization and Segmentation Graphs

Hubbard

Berg

Zhao

et al. 2020

Preprint

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Recent advances in automatic image segmentation and synapse prediction in electron microscopy (EM) datasets of the brain enable more efficient reconstruction of neural connectivity. In these datasets, a single neuron can span thousands of images containing complex tree-like arbors with thousands of synapses. While image segmentation algorithms excel within narrow fields of views, the algorithms sometimes struggle to correctly segment large neurons, which require large context given their size and complexity. Conversely, humans are comparatively good at reasoning with large objects. In this paper, we introduce several semi-automated strategies that combine 3D visualization and machine guidance to accelerate connectome reconstruction. In particular, we introduce a strategy to quickly correct a segmentation through merging and cleaving, or splitting a segment along supervoxel boundaries, with both operations driven by affinity scores in the underlying segmentation. We deploy these algorithms as streamlined workflows in a tool called Neu3 and demonstrate superior performance compared to prior work, thus enabling efficient reconstruction of much larger datasets. The insights into proofreading from our work clarify the trade-offs to consider when tuning the parameters of image segmentation algorithms.

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