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Related Concept Videos

Classification of Epithelial Tissues: Simple Epithelium01:30

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Simple epithelium consists of a single layer of cells that lines body cavities and blood vessels. The shape of the cells in the epithelium reflects the function of the tissue. Cells in simple squamous epithelium appear as thin scales with flat, elliptical nuclei that mirror the form of the cell.
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Stratified epithelium consists of several stacked layers of cells. They provide the durability to withstand constant physical and chemical attacks. Stratified epithelium is named after the shape of the most apical layer of cells. Stratified squamous epithelium is the most common type found in the human body. In this tissue, the apical cells are squamous, whereas the basal layer contains either columnar or cuboidal cells. The basal cells divide to form new daughter cells, which gradually become...
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EPySeg: a coding-free solution for automated segmentation of epithelia using deep learning.

Benoit Aigouy1, Claudio Cortes1, Shanda Liu2

  • 1Aix Marseille University, CNRS, IBDM, 13288 Marseille, France.

Development (Cambridge, England)
|December 3, 2020
PubMed
Summary

EPySeg is a new software tool that automatically segments epithelial tissues using deep learning. This coding-free solution significantly reduces manual correction, accelerating developmental biology research.

Keywords:
Computer visionDeep learningEpitheliaQuantitative biologySegmentationSoftware

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Area of Science:

  • Developmental Biology
  • Cell Biology
  • Bioimage Analysis

Background:

  • Epithelial tissues are dynamic and self-remodel during development.
  • Tissue-scale organization arises from individual cell contributions.
  • Accurate cell segmentation is crucial for understanding morphogenesis but is labor-intensive.

Purpose of the Study:

  • To present EPySeg, an open-source software for automated segmentation of epithelial tissues.
  • To provide a user-friendly, coding-free solution for developmental biologists.
  • To reduce the manual effort and time required for cell segmentation in epithelial tissues.

Main Methods:

  • Deep learning-based image segmentation.
  • Development of EPySeg software with a graphical user interface.
  • Compatibility as a Python package for local use or cloud-based via Google Colab.

Main Results:

  • EPySeg enables automatic and highly efficient segmentation of membrane-stained epithelial tissues.
  • The software substantially reduces the need for manual correction in image segmentation.
  • Accelerated and improved characterization of epithelial tissues is achieved.

Conclusions:

  • EPySeg offers a powerful, accessible tool for developmental biologists.
  • Automated segmentation with EPySeg streamlines research workflows.
  • The software enhances the study of epithelial tissue dynamics and morphogenesis.