Evidence map›Paper›PMID 42056271›Full record

ArticleScientific reports2026

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

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

Abstract read
In one paragraph

Article in Scientific reports, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0cells of the map it votes in
0citing papers in PubMed
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1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

9 authors.

Jacob Licea-RodriguezCentro de Investigación en Ingeniería y Ciencias Aplicadas, Universidad Autónoma del Estado de Morelos, Av. Universidad 1001, 62209, Cuernavaca, México. jacob.licea@uaem.mx.
Gustavo Castro-OlveraICFO - Institut de Ciències Fotòniques, The Barcelona Institute of Science and Technology, 08860, Castelldefels, Spain.
Omar Palillero-SandovalCentro de Investigación en Ingeniería y Ciencias Aplicadas, Universidad Autónoma del Estado de Morelos, Av. Universidad 1001, 62209, Cuernavaca, México.
Martin EriksenInstitut de Física d'Altes Energies (IFAE), The Barcelona Institute of Science and Technology, Campus UAB, 08193, Bellaterra (Barcelona), Spain.
Gonzalo MerinoPort d'Informació Científica (PIC), Campus UAB, C. Albareda S/N, 08193, Bellaterra (Barcelona), Spain.
Roberto A Beltran-VargasCentro de Investigación en Ingeniería y Ciencias Aplicadas, Universidad Autónoma del Estado de Morelos, Av. Universidad 1001, 62209, Cuernavaca, México.
Israel Rocha-MendozaCentro de Investigación Científica y de Educación Superior de Ensenada, Carretera Ensenada-Tijuana, No. 3918, Zona Playitas, 22860, Ensenada B. C., México.
Omar E OlarteDepartamento de Física, Universidad Nacional de Colombia, 111321, Bogotá, Colombia.
Pablo Loza-AlvarezICFO - Institut de Ciències Fotòniques, The Barcelona Institute of Science and Technology, 08860, Castelldefels, Spain. pablo.loza@icfo.eu.

Funding

Consejo Nacional de Humanidades, Ciencias y Tecnologías 155803Consejo Nacional de Humanidades, Ciencias y Tecnologías 265120Departament de Recerca i Universitats, Generalitat de Catalunya 2021 SGR 01456Horizon 2020 871277Laserlab-Europe 871124Ministerio de Asuntos Económicos y Transformación Digital, Gobierno de España CEX2019-000910-SMinisterio de Ciencia e Innovación PID2021-122807OB-C31Ministerio de Ciencia e Innovación PID2023-152069NA-I00
6 · The paper itself

Abstract

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.

Identifiers

PMID42056271
PMCPMC13314992

What Socratic holds

Textmetadata
LicenceCC BY-NC-ND
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.