Robust microbial cell segmentation by optical-phase thresholding with minimal processing requirements.

Area of Science:

• Microscopy and Imaging Technologies
• Cell Biology
• Systems Biology

Background:

• High-throughput imaging with single-cell resolution is crucial for cell physiology and Systems Biology.
• Accurate cell segmentation from images is a common and challenging bottleneck in image analysis.
• Existing methods often require computationally intensive post-acquisition processing.

Purpose of the Study:

• To develop a robust and efficient cell segmentation strategy for microbial cells.
• To improve segmentation success rates for yeast and bacterial cells.
• To reduce computational requirements compared to existing segmentation methods.

Main Methods:

• Utilized Quantitative Phase Imaging (QPI) for microbial cell imaging.
• Developed a segmentation strategy leveraging the innate properties of QPI.
• Applied the method to yeast and bacterial cell samples.

Main Results:

• Achieved a yeast cell segmentation success rate exceeding 99%.
• Demonstrated a bacterial cell segmentation success rate of 98%.
• The method requires no computationally-intensive, post-acquisition processing and reduces processing requirements compared to existing techniques.

Conclusions:

• The proposed QPI-based segmentation strategy is highly accurate and efficient for microbial cells.
• The method's success is attributed to QPI's uniform background, artifact elimination, and enhanced signal-to-background ratio.
• This approach offers a significant advancement for high-throughput cell imaging analysis.