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Volume Segmentation and Analysis of Biological Materials Using SuRVoS Super-region Volume Segmentation Workbench

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The urine formed element instance segmentation based on YOLOv5n.

Shuqin Tu¹, Hongxing Liu¹, Liang Mao²

¹College of Mathematics and Informatics, South China Agricultural University, Guangzhou, 510642, China.

Scientific Reports

|November 20, 2024

Summary

This summary is machine-generated.

This study introduces a fast deep learning model, YOLOv5n, for urine analysis. It significantly improves the speed and accuracy of detecting and segmenting urine elements, aiding clinical diagnosis.

Keywords:

Fast Instance segmentation Urine formed element YOLOv5n

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Area of Science:

Medical Imaging and Diagnostics
Computer Vision
Artificial Intelligence in Healthcare

Background:

Manual microscopy for urine analysis is time-consuming and subjective.
Current automated methods struggle with accuracy and speed for small, dense urine elements.
Accurate detection and segmentation of urine elements are crucial for diagnosing urinary and kidney diseases.

Purpose of the Study:

To develop a rapid and accurate instance segmentation model for urine formed elements.
To address the limitations of existing algorithms in speed and precision for urine analysis.
To leverage deep learning for automated urine particle detection.

Main Methods:

Proposed a one-stage instance segmentation model based on YOLOv5n for urine formed elements.
Utilized a backbone network for feature extraction (shallow graphical and semantic features).
Employed a neck network for multi-scale feature fusion and a head network integrating FCN for detection and segmentation.

Main Results:

Achieved 91.8% Mean Average Precision (mAP50) and 63.3 Frames Per Second (FPS) on a custom dataset.
Demonstrated significant speed improvements (60.9-62.6% faster) compared to Mask R-CNN and YOLOv8.
Showcased superior accuracy (1.4-3.6% mAP50 increase) and speed balance against other state-of-the-art models.

Conclusions:

The YOLOv5n model offers a highly accurate and efficient solution for automated urine formed element analysis.
This deep learning approach provides technical support for clinical disease diagnosis through faster, more precise urine analysis.
The developed method shows promise for widespread adoption in automated urine analysis systems.