ArticleScientific data2023
VISEM-Tracking, a human spermatozoa tracking dataset.
Article in Scientific data, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
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Who cites it
14 citing papers in PubMed, 45 citations in OpenAlex.
- Current state of artificial intelligence applications in assisted reproductive technology: A narrative review.Clinical and experimental reproductive medicine · 2026Article
- 3D+t human sperm flagellum centerline dataset.Scientific data · 2026Article
- A framework for evaluating predicted sperm trajectories in crowded microscopy videos.PLoS computational biology · 2026Article
- BehaveAI enables rapid detection and classification of objects and behavior from motion.PLoS biology · 2026Article
- Robust Real-Time Sperm Tracking with Identity Reassignment Using Extended Kalman Filtering.Sensors (Basel, Switzerland) · 2025Article
- Deep learning-based approach for sperm morphology analysis.BMC urology · 2025Review
- Improving Cell Detection and Tracking in Microscopy Images Using YOLO and an Enhanced DeepSORT Algorithm.Sensors (Basel, Switzerland) · 2025Article
- 3D+t Multifocal Imaging Dataset of Human Sperm.Scientific data · 2025Article
- Deep Learning Models for Multi-Part Morphological Segmentation and Evaluation of Live Unstained Human Sperm.Sensors (Basel, Switzerland) · 2025Article
- Artificial intelligence model for the assessment of unstained live sperm morphology.Reproduction & fertility · 2025Article
- Extender development for optimal cryopreservation of buck sperm to increase reproductive efficiency of goats.Frontiers in veterinary science · 2025Review
- [Application of Artificial Intelligence in Sperm Quality Analysis and Sperm Screening].Sichuan da xue xue bao. Yi xue ban = Journal of Sichuan University. Medical science edition · 2024Review
- Biomarker-based human and animal sperm phenotyping: the good, the bad and the ugly†.Biology of reproduction · 2024Review
- Sperm YOLOv8E-TrackEVD: A Novel Approach for Sperm Detection and Tracking.Sensors (Basel, Switzerland) · 2024Article
Corrections and comments
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Authors and funding
10 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
A manual assessment of sperm motility requires microscopy observation, which is challenging due to the fast-moving spermatozoa in the field of view. To obtain correct results, manual evaluation requires extensive training. Therefore, computer-aided sperm analysis (CASA) has become increasingly used in clinics. Despite this, more data is needed to train supervised machine learning approaches in order to improve accuracy and reliability in the assessment of sperm motility and kinematics. In this regard, we provide a dataset called VISEM-Tracking with 20 video recordings of 30 seconds (comprising 29,196 frames) of wet semen preparations with manually annotated bounding-box coordinates and a set of sperm characteristics analyzed by experts in the domain. In addition to the annotated data, we provide unlabeled video clips for easy-to-use access and analysis of the data via methods such as self- or unsupervised learning. As part of this paper, we present baseline sperm detection performances using the YOLOv5 deep learning (DL) model trained on the VISEM-Tracking dataset. As a result, we show that the dataset can be used to train complex DL models to analyze spermatozoa.
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