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A Rapid Method for Multispectral Fluorescence Imaging of Frozen Tissue Sections

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Asbestos Detection with Fluorescence Microscopy Images and Deep Learning.

Changjie Cai¹, Tomoki Nishimura², Jooyeon Hwang¹

¹Department of Occupational and Environmental Health, University of Oklahoma Health Sciences Center, University of Oklahoma, Oklahoma City, OK 73069, USA.

Sensors (Basel, Switzerland)

|July 20, 2021

Summary

This study introduces YOLOv4, a deep learning model, for accurate asbestos detection using fluorescence microscopy. The AI model significantly improves fiber counting accuracy, especially for low concentrations, outperforming previous software.

Keywords:

Convolutional Neural Networks (CNN)YOLOv4 asbestos fluorescence microscopy

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Area of Science:

Environmental Science
Occupational Health
Analytical Chemistry

Background:

Fluorescent probes enable asbestos detection, but previous software struggled with low fiber concentrations.
Machine learning, particularly Convolutional Neural Networks (CNNs), shows promise for image analysis in various fields.

Purpose of the Study:

To develop a comprehensive database of fluorescence microscopy (FM) images of asbestos across a range of concentrations (0-50 fibers/liter).
To evaluate the effectiveness of the YOLOv4 object detection CNN model for accurate asbestos detection and quantification.

Main Methods:

Created a labeled database of FM images containing asbestos fibers.
Trained the YOLOv4 model using a Graphics Processing Unit (GPU).
Validated performance against the National Institute for Occupational Safety and Health (NIOSH) Method 7400.

Main Results:

YOLOv4 demonstrated exceptional ability to identify fluorescent asbestos morphologies.
Achieved a mean average precision (mAP@0.5) of 96.1% ± 0.4%.
Significantly outperformed previous software (Intec/HU) in accuracy, precision, recall, and F-1 score, particularly for samples with <15 fibers/liter.

Conclusions:

The combination of FM and the YOLOv4 model provides a highly effective method for asbestos detection.
YOLOv4 significantly enhances the accuracy of asbestos fiber counting, especially at low concentrations.
This approach is valuable for differentiating asbestos from other non-asbestos particles.