@article {68258, title = {NeuronBridge: an intuitive web application for neuronal morphology search across large data sets}, journal = {BMC Bioinformatics}, volume = {25}, year = {2024}, month = {2024 Mar 15}, pages = {114}, abstract = {

Background

Neuroscience research in\ Drosophila\ is benefiting from large-scale connectomics efforts using electron microscopy (EM) to reveal all the neurons in a brain and their connections. To exploit this knowledge base, researchers relate a connectome{\textquoteright}s structure to neuronal function, often by studying individual neuron cell types. Vast libraries of fly driver lines expressing fluorescent reporter genes in sets of neurons have been created and imaged using confocal light microscopy (LM), enabling the targeting of neurons for experimentation. However, creating a fly line for driving gene expression within a single neuron found in an EM connectome remains a challenge, as it typically requires identifying a pair of driver lines where only the neuron of interest is expressed in both. This task and other emerging scientific workflows require finding similar neurons across large data sets imaged using different modalities.

Results

Here, we present NeuronBridge, a web application for easily and rapidly finding putative morphological matches between large data sets of neurons imaged using different modalities. We describe the functionality and construction of the NeuronBridge service, including its user-friendly graphical user interface (GUI), extensible data model, serverless cloud architecture, and massively parallel image search engine.

Conclusions

NeuronBridge fills a critical gap in the\ Drosophila\ research workflow and is used by hundreds of neuroscience researchers around the world. We offer our software code, open APIs, and processed data sets for integration and reuse, and provide the application as a service at\ http://neuronbridge.janelia.org.

}, doi = {10.1186/s12859-024-05732-7}, url = {https://bmcbioinformatics.biomedcentral.com/articles/10.1186/s12859-024-05732-7}, author = {Jody Clements and Cristian Goina and Philip M. Hubbard and Takashi Kawase and Donald J. Olbris and Hideo Otsuna and Robert Svirskas and Konrad Rokicki} } @article {68841, title = {Accelerated EM Connectome Reconstruction using 3D Visualization and Segmentation Graphs}, journal = {bioRxiv}, year = {2020}, month = {2020 Jan 17}, abstract = {

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.

}, doi = {10.1101/2020.01.17.909572}, url = {https://doi.org/10.1101/2020.01.17.909572}, author = {Philip M. Hubbard and Stuart Berg and Ting Zhao and Donald J. Olbris and Lowell Umayam and Jeremy Maitin-Shepard and Michal Januszewski and William T. Katz and Erika R. Neace and Stephen M. Plaza} }