# VISEM-Tracking, a human spermatozoa tracking dataset

arXiv: https://arxiv.org/abs/2212.02842 · HTML: https://mlsanity.com/paper/2212.02842/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 26 references that are in the map.
- First posted on arXiv 6 Dec 2022.

## Builds on (most cited first, 7 of 7)

- [Denoising Diffusion Probabilistic Models](https://mlsanity.com/paper/2006.11239/) (2020)
- [Generative Adversarial Networks](https://mlsanity.com/paper/2203.00667/) (2022)
- [Conditional Generative Adversarial Nets](https://mlsanity.com/paper/1411.1784/) (2014)
- [D2C: Diffusion-Denoising Models for Few-shot Conditional Generation](https://mlsanity.com/paper/2106.06819/) (2021)
- [Machine Learning-Based Analysis of Sperm Videos and Participant Data for Male Fertility Prediction](https://mlsanity.com/paper/1910.13327/) (2019)
- [Stacked dense optical flows and dropout layers to predict sperm motility and morphology](https://mlsanity.com/paper/1911.03086/) (2019)
- [Extracting temporal features into a spatial domain using autoencoders for sperm video analysis](https://mlsanity.com/paper/1911.03100/) (2019)

## Built on by (most cited first, 2 of 2)

- [Point, Detect, Count: Multi-Task Medical Image Understanding with Instruction-Tuned Vision-Language Models](https://mlsanity.com/paper/2505.16647/) (2025)
- [CHAMMI-75: Pre-training multi-channel models with heterogeneous microscopy images](https://mlsanity.com/paper/2512.20833/) (2025)

## Abstract (arXiv)

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-assisted 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 sperm 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.