# Automated identification and segmentation of urine spots based on deep-learning

## TL;DR

This paper introduces a deep learning system to automatically detect and measure urine spots in rodent studies, improving accuracy and efficiency over traditional methods.

## Contribution

The novel contribution is a deep learning-based system for automated urine spot detection and segmentation, reducing subjective errors and enabling precise quantitative analysis.

## Key findings

- The system accurately detects and segments urine spots, reducing subjective errors in traditional methods.
- It effectively quantifies overlapping urine spots and determines urination times with high precision.
- The approach supports high-throughput analysis of urination behavior in rodents.

## Abstract

Micturition serves an essential physiological function that allows the body to eliminate metabolic wastes and maintain water-electrolyte balance. The urine spot assay (VSA), as a simple and economical assay, has been widely used in the study of micturition behavior in rodents. However, the traditional VSA method relies on manual judgment, introduces subjective errors, faces difficulty in obtaining appearance time of each urine spot, and struggles with quantitative analysis of overlapping spots. To address these challenges, we developed a deep learning-based approach for the automatic identification and segmentation of urine spots. Our system employs a target detection network to efficiently detect each urine spot and utilizes an instance segmentation network to achieve precise segmentation of overlapping urine spots. Compared with the traditional VSA method, our system achieves automated detection of urine spot area of micturition in rodents, greatly reducing subjective errors. It accurately determines the urination time of each spot and effectively quantifies the overlapping spots. This study enables high-throughput and precise urine spot detection, providing important technical support for the analysis of urination behavior and the study of the neural mechanism underlying urination.