Scholarlink

<p>We present a wavefront coding (WFC) based decoupled illumination-detection light sheet fluorescence microscopy (DID-LSFM) system that integrates machine learning for high-speed volumetric imaging. This approach enables high-speed volumetric imaging of three-dimensional (3D) sperm flagellar dynamics at up to 80 volumes/s, more than twice the typical volumetric video rate. The speed is enabled by wavefront-coded DID-LSFM and extended depth-of-field imaging, whereas the image quality is significantly enhanced by applying machine learning-based denoising prior to deconvolution. By incorporating a cubic phase mask for WFC, we extend the depth of field (DoF) of a high numerical aperture (NA = 1.0) collection objective from 2.6 up to 40&#xa0;µm, more than an order of magnitude increase. Image sharpness and contrast are restored through digital deconvolution using measured and simulated point spread functions (PSFs). Because deconvolution is highly noise-sensitive, we incorporate a self-supervised machine learning-based denoising algorithm (Noise2Void) applied to the raw sperm images prior to deconvolution. The PSFs of the generated cubic phase masks were denoised using a classical non-local means filter implemented in scikit-image, followed by median background subtraction. This approach was selected as a conservative and deterministic method that reduces noise while preserving the characteristic PSF structure required for reliable deconvolution. This denoising step significantly enhances the reconstruction fidelity and stability. We conduct a comparative analysis of images restored using both measured and simulated PSFs and provide a quantitative assessment of the resolution achieved using the cubic phase mask. Fast, high-contrast volumetric sperm imaging is essential for assessing fertility and reproductive health. The proposed method provides an approach to visualize the 3D sperm flagellar dynamics, information that is not include in Computer-Assisted Sperm Analysis (CASA). The proposed imaging system combined with CASA could have the potential to provide an advanced platform for quantitative reproductive studies.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Fast 3D dynamic visualization of porcine spermatozoa by using high-resolution wavefront coding light sheet microscopy and machine learning

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年4月29日

查看全文
  • Jacob Licea-Rodriguez,
  • Gustavo Castro-Olvera,
  • Omar Palillero-Sandoval,
  • Martin Eriksen,
  • Gonzalo Merino,
  • Roberto A. Beltran-Vargas,
  • Israel Rocha-Mendoza,
  • Omar E. Olarte,
  • Pablo Loza-Alvarez

摘要

We present a wavefront coding (WFC) based decoupled illumination-detection light sheet fluorescence microscopy (DID-LSFM) system that integrates machine learning for high-speed volumetric imaging. This approach enables high-speed volumetric imaging of three-dimensional (3D) sperm flagellar dynamics at up to 80 volumes/s, more than twice the typical volumetric video rate. The speed is enabled by wavefront-coded DID-LSFM and extended depth-of-field imaging, whereas the image quality is significantly enhanced by applying machine learning-based denoising prior to deconvolution. By incorporating a cubic phase mask for WFC, we extend the depth of field (DoF) of a high numerical aperture (NA = 1.0) collection objective from 2.6 up to 40 µm, more than an order of magnitude increase. Image sharpness and contrast are restored through digital deconvolution using measured and simulated point spread functions (PSFs). Because deconvolution is highly noise-sensitive, we incorporate a self-supervised machine learning-based denoising algorithm (Noise2Void) applied to the raw sperm images prior to deconvolution. The PSFs of the generated cubic phase masks were denoised using a classical non-local means filter implemented in scikit-image, followed by median background subtraction. This approach was selected as a conservative and deterministic method that reduces noise while preserving the characteristic PSF structure required for reliable deconvolution. This denoising step significantly enhances the reconstruction fidelity and stability. We conduct a comparative analysis of images restored using both measured and simulated PSFs and provide a quantitative assessment of the resolution achieved using the cubic phase mask. Fast, high-contrast volumetric sperm imaging is essential for assessing fertility and reproductive health. The proposed method provides an approach to visualize the 3D sperm flagellar dynamics, information that is not include in Computer-Assisted Sperm Analysis (CASA). The proposed imaging system combined with CASA could have the potential to provide an advanced platform for quantitative reproductive studies.