@article {68635, title = {OME-Zarr: a cloud-optimized bioimaging file format with international community support.}, journal = {Histochemistry and Cell Biology}, volume = {160}, year = {2023}, month = {2023 Feb 25}, pages = {223-251}, abstract = {

A growing community is constructing a next-generation file format (NGFF) for bioimaging to overcome problems of scalability and heterogeneity. Organized by the Open Microscopy Environment (OME), individuals and institutes across diverse modalities facing these problems have designed a format specification process (OME-NGFF) to address these needs. This paper brings together a wide range of those community members to describe the format itself {\textendash} OME-Zarr {\textendash} along with tools and data resources available today to increase FAIR access and remove barriers in the scientific process. The current momentum offers an opportunity to unify a key component of the bioimaging domain {\textemdash} the file format that underlies so many personal, institutional, and global data management and analysis tasks.

}, doi = {10.1007/s00418-023-02209-1}, url = {https://doi.org/10.1007/s00418-023-02209-1}, author = {Josh Moore and Daniela Basurto-Lozada and S{\'e}bastien Besson and John Bogovic and Eva M. Brown and Jean-Marie Burel and Gustavo de Medeiros and Erin E. Diel and David Gault and Satrajit S. Ghosh and Ilan Gold and Yaroslav O. Halchenko and Matthew Hartley and Dave Horsfall and Mark S. Keller and Mark Kittisopikul and Gabor Kovacs and Ayb{\"u}ke K{\"u}pc{\"u} Yolda{\c s} and Albane le Tournoulx de la Villegeorges and Tong Li and Prisca Liberali and Melissa Linkert and Dominik Lindner and Joel L{\"u}thi and Jeremy Maitin-Shepard and Trevor Manz and Matthew McCormick and Khaled Mohamed and William Moore and Bugra {\"O}zdemir and Constantin Pape and Lucas Pelkmans and Martin Prete and Tobias Pietzsch and Stephan Preibisch and Norman Rzepka and David R. Stirling and Jonathan Striebel and Christian Tischer and Daniel Toloudis and Petr Walczysko and Alan M. Watson and Frances Wong and Kevin A. Yamauchi and Omer Bayraktar and Muzlifah Haniffa and Stephan Saalfeld and Jason R. Swedlow} } @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} } @article {65001, title = {Automated reconstruction of a serial-section EM drosophila brain with flood-filling networks and local realignment.}, journal = {bioRxiv}, year = {2019}, month = {2019 Apr 11}, abstract = {

Reconstruction of neural circuitry at single-synapse resolution is an attractive target for improving understanding of the nervous system in health and disease. Serial section transmission electron microscopy (ssTEM) is among the most prolific imaging methods employed in pursuit of such reconstructions. We demonstrate how Flood-Filling Networks (FFNs) can be used to computationally segment a forty-teravoxel whole-brain\ Drosophila\ ssTEM volume. To compensate for data irregularities and imperfect global alignment, FFNs were combined with procedures that locally re-align serial sections and dynamically adjust image content. The proposed approach produced a largely merger-free segmentation of the entire ssTEM\ Drosophila\ brain, which we make freely available. As compared to manual tracing using an efficient skeletonization strategy, the segmentation enabled circuit reconstruction and analysis workflows that were an order of magnitude faster.

}, doi = {10.1101/605634}, author = {Peter H. Li and Larry F. Lindsey and Micha{\l} Januszewski and Zhihao Zheng and Alexander Shakeel Bates and Istv{\'a}n Taisz and Mike Tyka and Matthew Nichols and Feng Li and Eric Perlman and Jeremy Maitin-Shepard and Tim Blakely and Laramie Leavitt and Gregory S.X.E. Jefferis and Davi Bock and Viren Jain} }