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Related Experiment Video

Updated: Feb 26, 2026

Measuring Sperm Guidance and Motility within the Caenorhabditis elegans Hermaphrodite Reproductive Tract

Measuring Sperm Guidance and Motility within the Caenorhabditis elegans Hermaphrodite Reproductive Tract

Published on: June 6, 2019

3D+t human sperm flagellum centerline dataset.

Paul Hernández-Herrera¹, Haydee O Hernández², Andres Bribiesca-Sanchez^3,4

¹Facultad de Ciencias, Universidad Autónoma de San Luis Potosí, San Luis Potosí, SLP, México. paul.hernandez@uaslp.mx.

Scientific Data

|February 24, 2026

Summary

Researchers created the first large-scale 3D+time dataset of human sperm flagella. This 3D-SpermFlagella dataset aids in understanding sperm motility and developing AI models for fertility research.

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Last Updated: Feb 26, 2026

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Area of Science:

Reproductive Biology
Biomedical Imaging
Computational Biology

Background:

Human sperm motility is vital for fertility, but traditional 2D analyses miss complex 3D flagellar movements.
Limited availability of comprehensive 3D+time sperm motility datasets hinders research progress.

Purpose of the Study:

Introduce 3D-SpermFlagella, the first large-scale 3D+time dataset of human sperm flagellum centerline annotations.
Provide a resource for studying sperm motility in full spatial complexity and for AI model development.

Main Methods:

Utilized multifocal video microscopy to capture sperm movement.
Annotated flagellar centerlines in 3D+time for 135 human sperm samples.
Included data under non-capacitating (NCC) and capacitating (CC) conditions, with coordinates in micrometers and voxels.

Main Results:

Established the 3D-SpermFlagella dataset, featuring detailed 3D+time flagellar centerline data.
Dataset includes annotations for sperm under both NCC and CC conditions.
Provides spatio-temporal data crucial for advanced motility analysis.

Conclusions:

The 3D-SpermFlagella dataset is a significant resource for advancing the study of human sperm motility.
Facilitates the development and benchmarking of AI-driven sperm tracking and segmentation models.
Enables deeper insights into the biomechanics of sperm movement and its role in fertility.