Accurately Analyze Fluorescent Widefield Images

How to remove out-of-focus blur, improve segmentation accuracy, and leverage the benefits of AI for image analysis

Left-hand image: The distribution of immune cells (white) and blood vessels (pink) in white adipose tissue (image captured using the THUNDER Imager 3D Cell Culture). Right-hand image: The same image after automated analysis using Aivia, with each immune cell color-coded based on its distance to the nearest blood vessel. Image courtesy of Dr. Selina Keppler, Munich, Germany. Fat_tissue_THUNDER_THUNDER-Aivia_teaser.jpg

The specificity of fluorescence microscopy allows researchers to accurately observe and analyze biological processes and structures quickly and easily, even when using thick or large samples. However, out-of-focus fluorescence increases background, reduces contrast, and makes accurate image segmentation more challenging. A powerful solution to these issues is the use of THUNDER to remove image blur in combination with Aivia which automates widefield image analysis using AI to improve speed and accuracy. Here, we explore this synergetic approach in more detail.

Get insights about

  • The challenges caused by blur when using widefield microscopy
  • Removing blur using advanced widefield imaging systems
  • Analyzing immune cell populations during inflammation

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