Non-Destructive and Real-Time Virtual Staining of Spermatozoa via Dark-Field Microscopy

Area of Science:

• Biomedical Engineering
• Reproductive Biology
• Computational Imaging

Background:

• Sperm morphology is vital for assessing fertilization potential.
• Traditional staining methods for sperm analysis cause irreversible damage and are time-consuming.
• There is a need for non-destructive, rapid sperm assessment techniques.

Purpose of the Study:

• To develop an improved Generative Adversarial Network (GAN) model for virtual staining of sperm.
• To enable real-time conversion of label-free sperm images into Papanicolaou-equivalent stained images.
• To provide a non-destructive method for sperm morphology assessment to aid in intracytoplasmic sperm injection (ICSI) selection.

Main Methods:

• Utilized a Generative Adversarial Network (GAN) model integrated with dark-field microscopy.
• Developed a virtual staining technique to transform label-free semen smears into high-contrast stained images.
• Quantified performance using mean squared error and structural similarity metrics compared to traditional Papanicolaou staining.

Main Results:

• The GAN model achieved real-time virtual staining in approximately 0.047 seconds for a 2048x2048 image.
• Mean squared error between virtual and true staining was 0.0044 ± 0.0031.
• Structural similarity index was high at 0.905 ± 0.015, indicating excellent image fidelity.

Conclusions:

• The developed virtual staining method offers a rapid, non-destructive alternative to traditional histological staining.
• This technology bypasses labor-intensive and costly procedures, enabling real-time analysis without laboratory quality control.
• The approach presents a novel strategy for sperm selection in assisted reproductive technologies like ICSI.