@article {69373, title = {Connectome-driven neural inventory of a complete visual system}, journal = {Nature}, year = {2024}, month = {2025 Mar 26}, abstract = {

Vision provides animals with detailed information about their surroundings, conveying diverse features such as color, form, and movement across the visual scene. Computing these parallel spatial features requires a large and diverse network of neurons, such that in animals as distant as flies and humans, visual regions comprise half the brain{\textquoteright}s volume. These visual brain regions often reveal remarkable structure-function relationships, with neurons organized along spatial maps with shapes that directly relate to their roles in visual processing. To unravel the stunning diversity of a complex visual system, a careful mapping of the neural architecture matched to tools for targeted exploration of that circuitry is essential. Here, we report a new connectome of the right optic lobe from a male Drosophila central nervous system FIB-SEM volume and a comprehensive inventory of the fly{\textquoteright}s visual neurons. We developed a computational framework to quantify the anatomy of visual neurons, establishing a basis for interpreting how their shapes relate to spatial vision. By integrating this analysis with connectivity information, neurotransmitter identity, and expert curation, we classified the 53,000 neurons into 727 types, about half of which are systematically described and named for the first time. Finally, we share an extensive collection of split-GAL4 lines matched to our neuron type catalog. Together, this comprehensive set of tools and data unlock new possibilities for systematic investigations of vision in Drosophila, a foundation for a deeper understanding of sensory processing.

}, doi = {10.1038/s41586-025-08746-0}, url = {https://doi.org/10.1038/s41586-025-08746-0}, author = {Aljoscha Nern and Frank Loesche and Shin-ya Takemura and Laura E Burnett and Marisa Dreher and Eyal Gruntman and Judith Hoeller and Gary B Huang and Michal Januszewski and Nathan C Klapoetke and Sanna Koskela and Kit D Longden and Zhiyuan Lu and Stephan Preibisch and Wei Qiu and Edward M Rogers and Pavithraa Seenivasan and Arthur Zhao and John Bogovic and Brandon S Canino and Jody Clements and Michael Cook and Samantha Finley-May and Miriam A Flynn and Imran Hameed and Kenneth J Hayworth and Gary Patrick Hopkins and Philip M Hubbard and William T Katz and Julie Kovalyak and Shirley A Lauchie and Meghan Leonard and Alanna Lohff and Charli A Maldonado and Caroline Mooney and Nneoma Okeoma and Donald J Olbris and Christopher Ordish and Tyler Paterson and Emily M Phillips and Tobias Pietzsch and Jennifer Rivas Salinas and Patricia K Rivlin and Ashley L Scott and Louis A Scuderi and Satoko Takemura and Iris Talebi and Alexander Thomson and Eric T Trautman and Lowell Umayam and Claire Walsh and John J Walsh and C Shan Xu and Emily A Yakal and Tansy Yang and Ting Zhao and Jan Funke and Reed George and Harald F Hess and Gregory S X E Jefferis and Christopher Knecht and Wyatt Korff and Stephen M Plaza and Sandro Romani and Stephan Saalfeld and Louis K Scheffer and Stuart Berg and Gerald M Rubin and Michael B Reiser} } @article {69473, title = {SciJava Ops: An Improved Algorithms Framework for Fiji and Beyond}, journal = {Front. Bioinform.}, volume = {4}, year = {2024}, month = {2024 Sep 26}, abstract = {

Decades of iteration on scientific imaging hardware and software has yielded an explosion in not only the size, complexity, and heterogeneity of image datasets but also in the tooling used to analyze this data. This wealth of image analysis tools, spanning different programming languages, frameworks, and data structures, is itself a problem for data analysts who must adapt to new technologies and integrate established routines to solve increasingly complex problems. While many {\textquotedblleft}bridge{\textquotedblright} layers exist to unify pairs of popular tools, there exists a need for a general solution to unify new and existing toolkits. The SciJava Ops library presented here addresses this need through two novel principles. Algorithm implementations are declared as plugins called Ops, providing a uniform interface regardless of the toolkit they came from. Users express their needs declaratively to the Op environment, which can then find and adapt available Ops on demand. By using these principles instead of direct function calls, users can write streamlined workflows while avoiding the translation boilerplate of bridge layers. Developers can easily extend SciJava Ops to introduce new libraries and more efficient, specialized algorithm implementations, even immediately benefitting existing workflows. We provide several use cases showing both user and developer benefits, as well as benchmarking data to quantify the negligible impact on overall analysis performance. We have initially deployed SciJava Ops on the Fiji platform, however it would be suitable for integration with additional analysis platforms in the future.

}, doi = {10.3389/fbinf.2024.1435733}, url = {https://doi.org/10.3389/fbinf.2024.1435733 }, author = {Gabriel J. Selzer and Curtis T. Rueden and Mark C. Hiner and Edward L. Evans III and David Kolb and Marcel Wiedenmann and Christian Birkhold and Tim-Oliver Buchholz and Stefan Helfrich and Brian Northan and Alison Walter and Johannes Schindelin and Tobias Pietzsch and Stephan Saalfeld and Michael R. Berthold and Kevin W. Eliceiri} } @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} } @conference {48180, title = {Tracking by assignment facilitates data curation.}, booktitle = {Medical Image Computing and Computer-Assisted Intervention {\textendash} MICCAI Workshop 2014}, year = {2014}, month = {2014 Sep 14}, address = {Boston, MA}, abstract = {

Object tracking is essential for a multitude of biomedical re- search projects. Automated methods are desired in order to avoid im- possible amounts of manual tracking efforts. However, automatically found solutions are not free of errors, and these errors again have to be identified and resolved manually. We propose six innovative ways for semi-automatic curation of automatically found tracking solutions. Respective user interactions are six simple operations: Inclusion and ex- clusion of objects and tracking decisions, specification of the number of objects, and one-click altering of object segmentations. We show how all proposed interactions can be elegantly incorporated into {\textquotedblleft}assignment models{\textquotedblright} [1,2,3,4,5,6], an innovative and increasingly popular tracking paradigm. Given some user interaction, the tracking engine is capable of computing the respective globally optimal tracking solution efficiently, even benefitting from {\textquotedblleft}warm start{\textquotedblright}-capabilities. We show that after in- teractively pointing at a single mistake, multiple segmentation and track- ing errors can be fixed automatically in one single re-evaluation, provably leading to the new, feedback-conscious global optimum.\ 

}, url = {http://interactivemedical.org/imic2014/CameraReadyPapers/Paper\%203/Jug_Interactive_IMIC2014.pdf}, author = {Jug, Florian and Tobias Pietzsch and Kainmueller, Dagmar and Myers, Eugene W} }