fishROI: a specialized workflow for semi-automated muscle morphometry analysis in teleosts

Abstract Background Quantitative histological analysis of skeletal muscle morphometry provides critical insights into muscle physiology but remains labor-intensive and technically demanding. While recent developments in machine learning-based image segmentation techniques have facilitated large-scale tissue analysis, existing tools that automate muscle morphometry analysis are largely tailored to mammalian models, with limited applicability to teleosts. Moreover, there is a lack of effective tools for visualizing spatial organization and morphometric variability of teleost muscle fibers, a feature that is important for understanding hyperplastic muscle growth dynamics in teleosts. Methods In this study, we benchmarked the performance of existing muscle morphometry analysis workflows and general-purpose cell segmentation algorithms in identifying individual muscle fibers in teleost muscles. We used these findings to inform the development of a new, teleost-optimized analysis tool. Results We show that cytoplasmic staining combined with machine learning-based cell segmentation offers a robust and accurate approach for automated muscle morphometry analysis in developing zebrafish. We also introduce fishROI – a Fiji plugin implemented in Jython, that streamlines both morphometric analysis and visualization. This tool accommodates shallow and deep learning-based segmentation techniques and incorporates novel quantification and visualization methods suited to teleost-specific muscle features, including mosaic hyperplasia dynamics. The plugin features an intuitive graphical user interface and is designed for flexibility, with minimal constraints regarding species, image quality, or staining protocol. Its modular architecture allows it to be used as a baseline for automated muscle morphometry analysis, while permitting integration with other tools and workflows to support a diverse range of analytical needs. Conclusion This plugin will serve as a foundational platform for semi-automated muscle morphometry analysis in teleosts, enabling a combination of reliable image segmentation, quantification and visualization optimized for teleost muscles.