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Fast 3D dynamic visualization of porcine spermatozoa by using high-resolution wavefront coding light sheet microscopy
Jacob Licea-Rodriguez1,2, Gustavo Castro-Olvera3, Omar Palillero-Sandoval4
1Centro de Investigación en Ingeniería y Ciencias Aplicadas, Universidad Autónoma del Estado de Morelos, Av. Universidad 1001, 62209, Cuernavaca, México. jacob.licea@uaem.mx.
Scientific Reports
|April 29, 2026
Summary
We developed a fast 3D sperm imaging system using wavefront coding and machine learning, achieving high-speed volumetric imaging of flagellar dynamics. This method significantly enhances image quality for improved fertility assessments.
Area of Science:
- Biomedical Imaging
- Microscopy
- Biophysics
Background:
- High-speed volumetric imaging is crucial for understanding 3D sperm flagellar dynamics.
- Current methods struggle to achieve sufficient speed and image quality for detailed analysis.
- Decoupled illumination-detection light sheet fluorescence microscopy (DID-LSFM) offers potential but requires further optimization.
Purpose of the Study:
- To develop a wavefront coding (WFC) based DID-LSFM system integrated with machine learning for high-speed 3D sperm imaging.
- To significantly extend the depth of field (DoF) and enhance image quality for improved visualization of sperm flagellar motion.
- To provide a novel imaging platform for quantitative reproductive studies beyond current Computer-Assisted Sperm Analysis (CASA) capabilities.
Main Methods:
- Implemented a WFC system using a cubic phase mask to extend the DoF of a high numerical aperture (NA) objective lens.
- Integrated a self-supervised machine learning-based denoising algorithm (Noise2Void) to improve image quality prior to deconvolution.
- Utilized digital deconvolution with measured and simulated point spread functions (PSFs) to restore image sharpness and contrast.
- Employed classical non-local means filtering and median background subtraction for denoising PSFs.
Main Results:
- Achieved high-speed volumetric imaging of 3D sperm flagellar dynamics at up to 80 volumes/s, more than double typical rates.
- Extended the DoF from 2.6 µm to 40 µm, an order of magnitude increase, using WFC.
- Significantly enhanced image sharpness, contrast, and reconstruction fidelity through machine learning-based denoising and deconvolution.
- Demonstrated the system's capability for fast, high-contrast volumetric sperm imaging essential for fertility assessment.
Conclusions:
- The proposed WFC-DID-LSFM system with machine learning integration enables unprecedented high-speed volumetric imaging of sperm dynamics.
- This advanced imaging approach significantly improves resolution and image quality, capturing details missed by conventional methods like CASA.
- The system holds potential as a powerful tool for quantitative reproductive health research and fertility diagnostics.
