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Imaging Biological Samples with Optical Microscopy

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Optical microscopy uses optic principles to provide detailed images of samples. Antonie van Leeuwenhoek designed the first compound optical microscope in the 17th century to visualize blood cells, bacteria, and yeast cells. In 1830, Joseph Jackson Lister created an essentially modern light microscope. The 20th century saw the development of microscopes with enhanced magnification and resolution.
In optical microscopy, the specimen to be viewed is placed on a glass slide and clipped on the stage...

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Analysis of Multidimensional Microscopy Data Using Cell-ACDC

Analysis of Multidimensional Microscopy Data Using Cell-ACDC

Published on: November 7, 2025

Anima: modular workflow system for comprehensive image data analysis.

Ville Rantanen¹, Miko Valori¹, Sampsa Hautaniemi¹

¹Research Programs Unit, Genome-Scale Biology and Institute of Biomedicine, Biochemistry and Developmental Biology, University of Helsinki , Helsinki , Finland.

Frontiers in Bioengineering and Biotechnology

|August 16, 2014

Summary

This summary is machine-generated.

Anima is a new modular workflow system designed for efficient, large-scale image data analysis. It simplifies complex projects with features like batch processing and interoperability, aiding scientific discovery.

Keywords:

automated analysis high-throughput image analysis quantification superplatform

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Area of Science:

Microscopy and Computational Biology
Bioimage Analysis

Background:

Modern microscopy generates massive datasets requiring sophisticated computational analysis.
Complex image analysis projects involve numerous sequential steps, from pre-processing to reporting.

Purpose of the Study:

To introduce Anima, a modular workflow system for comprehensive and efficient development of large-scale image data analysis projects.
To address the challenges of high-throughput image data analysis.

Main Methods:

Development of Anima, a modular workflow system.
Implementation of features including programming language independence, batch processing, customizable data processing, API interoperability, and advanced statistical analysis.
Demonstration of Anima's utility through two case studies.

Main Results:

Anima facilitates efficient development of complex image data analysis pipelines.
Case studies demonstrated Anima's capability in testing algorithms across platforms and automated prediction of C. elegans worm viability.
The system integrates various analysis environments for streamlined workflows.

Conclusions:

Anima provides a robust and flexible solution for managing and executing large-scale bioimage analysis projects.
The system's features support high-throughput analysis and interoperability, advancing computational biology research.
Anima is an open-source tool available for broader scientific adoption.