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HusMorph: a simple machine learning app for automated morphometric landmarking.

Henning H Kristiansen^1,2, Moa Metz¹, Lorena Silva-Garay¹

¹Department of Biology, Norwegian University of Science and Technology, Høgskoleringen 5, 7034 Trondheim, Norway.

Conservation Physiology

|October 24, 2025

Summary

This summary is machine-generated.

HusMorph is a new application that uses machine learning and computer vision to quickly measure animal traits from photos. This tool automates morphometric analysis, achieving high accuracy for large datasets.

Keywords:

Artificial intelligence automation images machine learning morphometrics phenotyping user-friendly

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Area of Science:

* Computational Biology
* Image Analysis
* Machine Learning

Background:

* Manual measurement of animal morphometric features is time-consuming and prone to inconsistency, especially with large datasets.
* High-throughput computing and machine learning offer potential for rapid and accurate phenotypic data collection.

Purpose of the Study:

* To introduce HusMorph, a novel, user-friendly application for automated morphometric analysis.
* To provide a machine learning-based tool that simplifies phenotypic data extraction for non-experts.

Main Methods:

* HusMorph utilizes a machine learning model trained on user-defined landmarks from standardized animal photos.
* The application features an automated parameter search for optimal model performance.
* It includes a scale bar detection feature for converting pixel measurements to metric units.

Main Results:

* HusMorph was validated on 1935 zebrafish photos for standard length extraction.
* The application achieved approximately 99.5% accuracy compared to manual measurements.
* The software provides a simple graphical user interface (GUI) for ease of use.

Conclusions:

* HusMorph offers an efficient and accurate solution for automated animal morphometric analysis.
* The tool democratizes the use of machine learning for phenotypic data collection.
* It significantly reduces the time and effort required for analyzing large image datasets.