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Related Experiment Video

Updated: Jan 19, 2026

Quantifying Intermembrane Distances with Serial Image Dilations
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Machine Learning Methods for Automated Quantification of Ventricular Dimensions.

Mark Schutera1, Steffen Just2, Jakob Gierten3,4

  • 1Institute for Automation and Applied Informatics (IAI), Karlsruhe Institute of Technology (KIT), Eggenstein, Germany.

Zebrafish
|September 20, 2019
PubMed
Summary

We developed a new framework to analyze medaka (Oryzias latipes) heart function. This method quantifies ventricular dimensions from image sequences, aiding cardiovascular disease research.

Keywords:
biomedical imagingdeep learningfractional shorteningmedakasegmentationzebrafish

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Last Updated: Jan 19, 2026

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Area of Science:

  • Cardiovascular research
  • Developmental biology
  • Biomedical imaging

Background:

  • Medaka (Oryzias latipes) and zebrafish (Danio rerio) are key models for studying human cardiovascular diseases.
  • Quantifying cardiac function is crucial for understanding disease mechanisms.

Purpose of the Study:

  • To develop a computational framework for analyzing medaka cardiac function.
  • To segment the medaka ventricle and quantify its dimensions from image sequences.

Main Methods:

  • Image segmentation algorithms applied to medaka hatchling video data.
  • Automated quantification of ventricular dimensions.

Main Results:

  • Successful segmentation of the medaka ventricle.
  • Quantification of key ventricular dimensions achieved.

Conclusions:

  • The developed framework enables precise measurement of medaka cardiac dimensions.
  • This tool supports research into the genetic and molecular basis of cardiovascular diseases using medaka models.