Skip to main content

ORIGINAL RESEARCH article

Front. Neural Circuits
Volume 18 - 2024 | doi: 10.3389/fncir.2024.1398884
This article is part of the Research Topic Structure, Function and Development of Neural Circuits View all 7 articles

Brain Image Data Processing Using Collaborative Data Workflows on Texera

Provisionally accepted
Yunyan Ding Yunyan Ding Yicong Huang Yicong Huang Pan Gao Pan Gao Andy Thai Andy Thai Atchuth N. Chilaparasetti Atchuth N. Chilaparasetti M Gopi M Gopi Xiangmin Xu Xiangmin Xu Chen Li Chen Li *
  • University of California, Irvine, Irvine, United States

The final, formatted version of the article will be published soon.

    In the realm of neuroscience, mapping the three-dimensional (3D) neural circuitry and architecture of the brain is important for advancing our understanding of neural circuit organization and function. This study presents a novel pipeline that transforms mouse brain samples into detailed 3D brain models using a collaborative data analytics platform called "Texera." The user-friendly Texera platform allows for effective interdisciplinary collaboration between team members in neuroscience, computer vision, and data processing. Our pipeline utilizes the tile images from a serial two-photon tomography/TissueCyte system, then stitches tile images into brain section images, and constructs 3D whole-brain image datasets. The resulting 3D data supports downstream analyses, including 3D whole-brain registration, atlas-based segmentation, cell counting, and high-resolution volumetric visualization. Using this platform, we implemented specialized optimization methods and obtained significant performance enhancement in workflow operations. We expect the neuroscience community can adopt our approach for large-scale image-based data processing and analysis.

    Keywords: Tissuecyte, Circuit tracing, mouse brain, data analytics, image stitching, 3D visualization

    Received: 10 Mar 2024; Accepted: 20 Jun 2024.

    Copyright: © 2024 Ding, Huang, Gao, Thai, Chilaparasetti, Gopi, Xu and Li. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

    * Correspondence: Chen Li, University of California, Irvine, Irvine, United States

    Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.