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REVIEW article

Front. Comput. Sci.
Sec. Computer Vision
Volume 6 - 2024 | doi: 10.3389/fcomp.2024.1156204
This article is part of the Research Topic Horizons in Computer Science 2022 View all 8 articles

Beyond Neurons: Computer Vision Methods for Analysis of Morphologically Complex Astrocytes

Provisionally accepted
  • Research Institute, McGill University Health Center, Montreal, Quebec, Canada

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

    The study of the geometric organization of biological tissues has a rich history in the literature. However, the geometry and architecture of individual cells within tissues has traditionally relied upon manual or indirect measures of shape. Such rudimentary measures are largely a result of challenges associated with acquiring high resolution images of cells and cellular components, as well as a lack of computational approaches to analyze large volumes of high-resolution data. This is especially true with brain tissue, which is composed of a complex array of cells. Here we review computational tools that have been applied to unravel the cellular nanoarchitecture of astrocytes, a type of brain cell that is increasingly being shown to be essential for brain function. Astrocytes are among the most structurally complex and functionally diverse cells in the mammalian body and are essential partner cells of neurons. Light microscopy does not allow adequate resolution of astrocyte morphology, however, large-scale serial electron microscopy data, which provides nanometer resolution 3D models, is enabling the visualization of the fine, convoluted structure of astrocytes. Application of computer vision methods to the resulting nanoscale 3D models is helping reveal the geometry and organizing principles of astrocytes, but a complete understanding of astrocyte structure and its functional implications will require further adaptation of existing computational tools, as well as development of new approaches.

    Keywords: astrocyte, Volume electron microscopy, connectomics, Brain, Computer Vision, shape, topology, Medial representations

    Received: 01 Feb 2023; Accepted: 18 Jun 2024.

    Copyright: © 2024 Salmon, Syed and Murai. 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: Christopher K. Salmon, Research Institute, McGill University Health Center, Montreal, H3H 2R9, Quebec, Canada

    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.