A Facile Method Based on Faster R-CNN for Cell Detection in Microfluidic Devices.

Area of Science:

• Microfluidics
• Cell Biology
• Bioimage Analysis

Background:

• Automated cell detection is crucial for analyzing microfluidic cell assays in various research fields.
• Conventional image processing struggles with microfluidic environments, necessitating advanced techniques.
• Machine learning methods offer solutions but often require extensive data labeling and coding skills.

Purpose of the Study:

• To present a facile method for cell detection in microfluidic arrays using Faster region-based convolutional neural network (R-CNN).
• To address the challenges of tedious image labeling and coding expertise in implementing machine learning for cell detection.
• To provide a ready-to-use model and guide for training Faster R-CNN without requiring coding expertise.

Main Methods:

• Utilized Faster region-based convolutional neural network (R-CNN) for cell detection in microfluidic arrays.
• Employed bounding boxes as labels for rapid and easy image annotation.
• Developed a ready-to-use model and a non-coding guide for training the Faster R-CNN model.

Main Results:

• Achieved cell detection with an average precision exceeding 98% using a few hundred annotations.
• Completed model training in less than 30 minutes, demonstrating high efficiency.
• Ensured accurate cell identification by preventing misidentification of microfluidic structures.

Conclusions:

• Faster R-CNN offers a precise and user-friendly solution for cell detection in microfluidic chips.
• This approach eliminates the need for extensive coding expertise and simplifies image labeling.
• The method is poised for broad application in on-chip fundamental biology and drug discovery assays.