Automated Detection of Zebrafish Larvae Tissue and Morphologies| in Microscopic Images

Zebrafish has become a valuable system for revealing biological cause-effect-dependencies. To raise significant statistical conclusions, data from numerous digital microscopies has to be analyzed, and high-throughput microscopy has become a means for raising data on a greater scale. However, data evaluation is often performed manually, which restricts the number of experiments that can be evaluated due to time-consuming processing steps.

The aim of the image analysis approach was to automatically detect the morphology of dechorionated zebrafish larvae at an age of 24-36 hours past fertilization (hpf). The analysis protocol was developed using Definiens Developer. The rule set first aligns the embryo, and and then detects cerebellum, yolk, yolk extension, heart region, and parts of the spinal cord as well as the notochord. This will enable researchers to automatically assign fluorescent activity in other channels to a tissue, and even quantify the activity in the corresponding tissue. This study proves that fully automated, detailed tissue segmentation in zebrafish is feasible. The processing of the rule set takes 12s, which also applies to high-throughput screens. The rule set is robust enough to be applied to embryos of different ages and positions.

Use case provided by Markus Reischl, Kai Hartmann, Rüdiger Alshut, Urban Liebel, and Ralf Mikut, Karlsruhe Institute of Technology, 76344 Eggenstein-Leopoldshafen, Germany