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<p>The study of human sperm motility has been a topic of interest for decades due to its crucial role in fertility and reproductive health. While most analyses rely on 2D+t imaging of head trajectories, sperm naturally swim in three dimensions (3D), driven by complex flagellar motion. However, the lack of comprehensive 3D+t datasets has limited progress in this field. To address this, we present <i>3D-SpermFlagella</i>, the first large-scale 3D+t dataset of human sperm flagellum centerline annotations. This dataset contains 135 tracked and annotated sperm, derived from our previously published multifocal video microscopy dataset <i>3D-SpermVid</i>. Each flagellar centerline was annotated over time in three dimensions, incubated under non-capacitating (NCC) and capacitating (CC) conditions. The (x,y,z) coordinates are provided in both micrometers and voxels, making <i>3D-SpermFlagella</i> a valuable resource for studying sperm motility in its full spatial complexity and for the development and benchmarking of AI-based models for tracking and segmentation. In this paper, we describe the segmentation and tracking methods, as well as the conditions and structure of the dataset.</p>

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3D+t human sperm flagellum centerline dataset

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年2月24日

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  • Paul Hernández-Herrera,
  • Haydee O. Hernández,
  • Andres Bribiesca-Sanchez,
  • Fernando Montoya,
  • Dan Sidney Díaz-Guerrero,
  • Alberto Darszon,
  • Gabriel Corkidi

摘要

The study of human sperm motility has been a topic of interest for decades due to its crucial role in fertility and reproductive health. While most analyses rely on 2D+t imaging of head trajectories, sperm naturally swim in three dimensions (3D), driven by complex flagellar motion. However, the lack of comprehensive 3D+t datasets has limited progress in this field. To address this, we present 3D-SpermFlagella, the first large-scale 3D+t dataset of human sperm flagellum centerline annotations. This dataset contains 135 tracked and annotated sperm, derived from our previously published multifocal video microscopy dataset 3D-SpermVid. Each flagellar centerline was annotated over time in three dimensions, incubated under non-capacitating (NCC) and capacitating (CC) conditions. The (x,y,z) coordinates are provided in both micrometers and voxels, making 3D-SpermFlagella a valuable resource for studying sperm motility in its full spatial complexity and for the development and benchmarking of AI-based models for tracking and segmentation. In this paper, we describe the segmentation and tracking methods, as well as the conditions and structure of the dataset.