Document Type

Article

Publication Date

3-1-2025

Publication Title

Physiol Rep

Keywords

JMG, Animals, Mice, Machine Learning, Urine, Urinalysis, Software, Urination, Mice, Inbred C57BL, Female, Neural Networks, Computer, Urinary Bladder

JAX Source

Physiol Rep. 2025;13(6):e70243.

Volume

13

Issue

6

First Page

70243

Last Page

70243

ISSN

2051-817X

PMID

40102661

DOI

https://doi.org/10.14814/phy2.70243

Grant

Jackson Laboratory (JAX), Grant/ Award Number: P30AG038070; HHS | NIH | National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK), Grant/Award Number: P20DK097818

Abstract

The void spot assay has gained popularity as a way of assessing functional bladder voiding parameters in mice, but analyzing the size and distribution of urine spot patterns on filter paper with software remains problematic due to inter-laboratory differences in image contrast and resolution quality and non-void artifacts. We have developed a machine learning algorithm based on Region-based Convolutional Neural Networks (Mask-RCNN) that was trained in object recognition to detect and quantitate urine spots across a broad range of sizes-ML-UrineQuant. The model proved extremely accurate at identifying urine spots in a wide variety of illumination and contrast settings. The overwhelming advantage it offers over current algorithms will be to allow individual labs to fine-tune the model on their specific images regardless of the image characteristics. This should be a valuable tool for anyone performing lower urinary tract research using mouse models.

Comments

This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided
the original work is properly cited

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