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Filtration and Urine Formation

Filtration and Urine Formation

The function of the kidneys is to filter, reabsorb, secrete, and excrete. Every day the kidneys filter nearly 180 liters of blood, initially removing water and solutes but ultimately returning nearly all filtrates into circulation with the help of osmoregulatory hormones. This process removes wastes and toxins but is also crucial to maintain water and electrolyte levels. Most of these functions are performed by the tiny but numerous nephrons contained within the kidneys.

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Urine Sediment Recognition Method Based on Multi-View Deep Residual Learning in Microscopic Image.

Xiaohong Zhang¹, Liqing Jiang¹, Dongxu Yang¹

¹Affiliated Hospital of Jining Medical University, Jining No.1 people's hospital, Jining, 272000, Shandong, China.

Journal of Medical Systems

|October 24, 2019

Summary

This study introduces a novel multi-view deep residual learning method for urine sediment recognition. The approach enhances accuracy and reduces computation time for urine cell analysis.

Keywords:

Convolutional network Deep residual Microscopic image Multi-View learning Squeeze-and-excitation Urine sediment

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Area of Science:

Computer Vision
Medical Image Analysis
Deep Learning

Background:

Urine sediment recognition is crucial for medical diagnostics.
Existing methods face challenges with cell gray changes and information loss.
Automated analysis requires robust feature extraction from multi-view images.

Purpose of the Study:

To propose a multi-view deep residual learning method for accurate urine sediment recognition.
To address limitations of existing methods, including cell gray variation and data loss.
To improve the efficiency and robustness of urine cell classification.

Main Methods:

Utilized a convolutional neural network (CNN) based on residual learning for feature extraction.
Incorporated depth-wise separable convolutions to minimize network parameters.
Employed Squeeze-and-Excitation blocks for feature re-calibration and spatial pyramid pooling for enhanced robustness.
Optimized network convergence using the Adam optimizer with weight decay.

Main Results:

Achieved state-of-the-art classification accuracy on a custom urine microscopic image dataset.
Demonstrated a significant reduction in network computing time.
The proposed method effectively handles multi-view cell gray changes and information loss.

Conclusions:

The multi-view deep residual learning method offers superior performance for urine sediment recognition.
This approach provides a more efficient and accurate solution for automated urine cell analysis.
The findings contribute to advancing computer vision applications in medical diagnostics.