Datasets:

sperm-net
/

VISEM-Tracking

---
license: cc
task_categories:
- object-detection
- visual-question-answering
language:
- en
tags:
- biology
- sperm
pretty_name: VISEM-Tracking
---

# Dataset Card for VISEM-Tracking

## Dataset Summary

VISEM-Tracking is a dataset of **human spermatozoa tracking**, featuring **20 video recordings of 30 seconds** each, captured from **wet semen preparations**. The dataset contains **29,196 frames** with **manually annotated bounding-box coordinates** and a set of **sperm characteristics** analyzed by domain experts. In addition to the labeled dataset, **unlabeled videos** are provided to enable research in **self-supervised learning**. This dataset serves as an extension of the **VISEM dataset**, incorporating **tracking data and additional clinical metadata**.

### Dataset Details

- **Curated by:** Vajira Thambawita, Steven A. Hicks, Andrea M. Storås, Thu Nguyen, Jorunn M. Andersen, Oliwia Witczak, Trine B. Haugen, Hugo L. Hammer, Pål Halvorsen, Michael A. Riegler.
- **Funded by:** Research Council of Norway under contract 270053.
- **Shared by:** Simula Metropolitan Center for Digital Engineering, Oslo Metropolitan University.
- **Language(s) (NLP):** Not applicable (biomedical imaging dataset).
- **License:** [Creative Commons Attribution 4.0 International (CC BY 4.0)](https://creativecommons.org/licenses/by/4.0/).

### Dataset Sources

- **Repository:** [Zenodo - VISEM-Tracking](https://zenodo.org/record/7293726)
- **Paper:** [Nature Scientific Data - VISEM-Tracking](https://doi.org/10.1038/s41597-023-02173-4)
- **Code Repository:** [VISEM-Tracking GitHub](https://github.com/simulamet-host/visem-tracking)

## Uses

### Direct Use

- **Sperm tracking and motility analysis** for **clinical and biomedical research**.
- **Machine learning model training** for **spermatozoa detection and tracking**.
- **Computer-aided sperm analysis (CASA)** system development.
- **Deep learning-based sperm motility assessment**.
- **Semi-supervised learning applications** using unlabeled data.

### Out-of-Scope Use

- Not designed for **direct clinical diagnosis**.
- Not suitable for **general object detection tasks** without domain-specific adaptation.

## Dataset Structure

The dataset consists of:
- **20 annotated 30-second videos** (45–50 FPS).
- **336 unlabeled 30-second videos** for self-supervised learning.
- **Bounding box annotations** (YOLO format) with tracking IDs.
- **Three sperm categories**:  
  - `0: Normal sperm`
  - `1: Sperm clusters`
  - `2: Small or pinhead sperm`
- **Metadata CSV files** containing:
  - **Semen analysis results** (e.g., sperm concentration, motility).
  - **Fatty acid composition** (serum & spermatozoa).
  - **Sex hormone levels**.
  - **Participant-related data**.

## Dataset Creation

### Curation Rationale

Manual sperm motility assessment is **challenging and time-consuming**, requiring **expert training**. VISEM-Tracking was created to provide a **large-scale, annotated dataset** for training **computer-aided sperm analysis (CASA) models**, enabling **automated sperm tracking and classification**.

### Source Data

#### Data Collection and Processing

- **Collected between 2008 and 2013** as part of a study on **male reproductive function and obesity**.
- **Videos captured at 400× magnification** using an **Olympus CX31 microscope** with a **UEye UI-2210C camera**.
- **Bounding box annotations created using LabelBox**, validated by domain experts.
- **Annotations include tracking IDs** for spermatozoa over time.

#### Who are the source data producers?

- **Collected from 20 male participants**, aged **18 years or older**.
- **Approved by the Regional Committee for Medical and Health Research Ethics, South East, Norway (REK 2008/3957)**.
- Participants **provided written informed consent** and **data was anonymized**.

### Annotations

#### Annotation process

- Bounding boxes were **manually annotated by data scientists**.
- **Three biologists verified annotations** for **accuracy and consistency**.
- Labels include **sperm class, movement tracking, and cluster categorization**.

#### Who are the annotators?

- Researchers from **Simula Metropolitan Center for Digital Engineering** and **Oslo Metropolitan University**.
- Experts in **male reproduction and computer vision**.

#### Personal and Sensitive Information

- **All participant data is anonymized**.
- **No personally identifiable information is included**.
- Contains **biological sample analysis data** (e.g., hormone levels, fatty acid composition).

## Bias, Risks, and Limitations

- The dataset **only includes 20 subjects**, limiting its **generalizability**.
- The dataset may **not fully capture sperm motility variations across populations**.
- Annotations are **limited to bounding boxes**, without **detailed morphological analysis**.

### Recommendations

- Users should **consider dataset limitations** when applying it to real-world clinical applications.
- Further **data collection and augmentation** are recommended for **improving model generalization**.

## Citation

If you use this dataset, please cite:

**BibTeX:**
```bibtex
@article{thambawita2023visem,
  author = {Thambawita, Vajira and Hicks, Steven A. and Storås, Andrea M. and Nguyen, Thu and Andersen, Jorunn M. and Witczak, Oliwia and Haugen, Trine B. and Hammer, Hugo L. and Halvorsen, Pål and Riegler, Michael A.},
  title = {VISEM-Tracking, a human spermatozoa tracking dataset},
  journal = {Scientific Data},
  volume = {10},
  year = {2023},
  doi = {10.1038/s41597-023-02173-4}
}
```

## More Information

For additional details, refer to:
- [Nature Scientific Data Paper](https://doi.org/10.1038/s41597-023-02173-4)
- [Zenodo Dataset](https://zenodo.org/record/7293726)
- [GitHub Repository](https://github.com/simulamet-host/visem-tracking)

## Dataset Card Authors

- Vajira Thambawita
- Steven A. Hicks
- Andrea M. Storås
- Hugo L. Hammer
- Michael A. Riegler

## Dataset Card Contact

For inquiries, contact:
- **Vajira Thambawita** ([email protected])
```