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HusMorph: A simple machine learning app for automated morphometric landmarking

HusMorph: A simple machine learning app for automated morphometric landmarking

This is a Preprint and has not been peer reviewed. This is version 2 of this Preprint.

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Authors

Henning Husmo Kristiansen, Moa Metz, Lorena Silva-Garay, Fredrik Jutfelt, Robine H. J. Leeuwis 

Abstract

Manually obtaining the length and other morphometric features of an animal can be time- consuming, and consistent measurements are challenging with large datasets. By leveraging high-throughput computing power and machine learning-based computer vision, such phenotypic data can be rapidly collected with high accuracy. Here we present HusMorph, a novel application with a simple and intuitive graphical user interface (GUI), based on the same machine learning method used in other pipelines such as ML-morph. It consists of an all-in-one package with the goal of making machine learning easy to use for non-experts. The user starts by setting any number of landmarks on a set of photos captured with a standardized setup. From this set, a machine learning model is generated by automatically and randomly searching for the best performing parameters. Next, the user can apply the model to predict landmarks on new standardized photos, and visually confirm and export the results of the predictions. For measuring length between landmarks, an additional feature allows for detecting a scale bar for each photo to convert the length from pixels to a metric unit. Our application has been validated and applied to extract standard length from 1,935 photos of zebrafish and performs with about 99.5% accuracy compared to manual measurements along with 100% scale bar detection.

DOI

https://doi.org/10.32942/X2F92H

Subjects

Artificial Intelligence and Robotics, Biology

Keywords

Artificial Intelligence, automation, images, morphometrics, Phenotyping, User-friendly

Dates

Published: 2025-02-21 21:39

Last Updated: 2025-02-26 21:50

Older Versions

License

No Creative Commons license

Additional Metadata

Language:
English

Data and Code Availability Statement:
The code repository for the project are openly available at GitHub: https://github.com/HenHus/Husmorph