ML‐UrineQuant: A machine learning program for identifying and quantifying mouse urine on absorbent paper

• Published in: Physiological reports Vol. 13; no. 6; pp. e70243 - n/a
• Main Authors: Hill, Warren G., MacIver, Bryce, Churchill, Gary A., DeOliveira, Mariana G., Zeidel, Mark L., Cicconet, Marcelo
• Format: Journal Article
• Language: English
• Published: United States John Wiley & Sons, Inc 01.03.2025; John Wiley and Sons Inc; Wiley
• Subjects: Algorithms; Animal models; Animals; artificial intelligence; Filter paper; Image Processing, Computer-Assisted - methods; Learning algorithms; Machine Learning; Methods; Mice; Mice, Inbred C57BL; micturition; Neural networks; Neural Networks, Computer; Pattern recognition; Polyuria; python; Regular Manuscript; Urinalysis - methods; Urinary tract; Urine; urology; void spot on paper; voiding dysfunction; VSA; VSOP; Fiji; python; void spot on paper; VSA; voiding dysfunction; VSOP; urology; mice; micturition; artificial intelligence
• ISSN: 2051-817X; 2051-817X
• DOI: 10.14814/phy2.70243

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.