VISEM-Tracking

Pre-print and citation:

[Pre-print](https://arxiv.org/abs/2212.02842)

@article{thambawita2023visem,
  title={VISEM-Tracking, a human spermatozoa tracking dataset},
  author={Thambawita, Vajira and Hicks, Steven A and Stor{\aa}s, Andrea M and Nguyen, Thu and Andersen, Jorunn M and Witczak, Oliwia and Haugen, Trine B and Hammer, Hugo L and Halvorsen, P{\aa}l and Riegler, Michael A},
  journal={Scientific Data},
  volume={10},
  number={1},
  pages={1--8},
  year={2023},
  publisher={Nature Publishing Group}
}

Motivation and background

Manual evaluation of a sperm sample using a microscope is time-consuming and requires costly experts who have extensive training. In addition, the validity of manual sperm analysis becomes unreliable due to limited reproducibility and high inter-personnel variations due to the complexity of tracking, identifying, and counting sperms in fresh samples. The existing computer-aided sperm analyzer systems are not working well enough for application in a real clinical setting due to unreliability caused by the consistency of the semen sample. Therefore, we need to research new methods for automated sperm analysis.

Target group

The task is of interest to researchers in the areas of machine learning (classification and detection), visual content analysis, and multimodal fusion. Overall, this task is intended to encourage the multimedia community to help improve the health care system through the application of their knowledge and methods to reach the next level of computer and multimedia-assisted diagnosis, detection, and interpretation.

Class Label Mapping
sperm: 0
cluster: 1
small or pinhead: 2