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Related Experiment Video

Updated: Aug 26, 2025

Fish Sperm Assessment Using Software and Cooling Devices

Fish Sperm Assessment Using Software and Cooling Devices

Published on: July 28, 2018

An assessment tool for computer-assisted semen analysis (CASA) algorithms.

Ji-Won Choi¹, Ludvik Alkhoury¹, Leonardo F Urbano²

¹Department of Electrical and Computer Engineering, New Jersey Institute of Technology, Newark, NJ, 07102, USA.

Scientific Reports

|October 7, 2022

Summary

This summary is machine-generated.

Computer-Assisted Semen Analysis (CASA) simulations provide a controllable method for testing sperm image analysis algorithms. These life-like simulations accelerate the development and validation of segmentation, localization, and tracking techniques for improved accuracy.

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Area of Science:

Biomedical Engineering
Computer Vision
Reproductive Medicine

Background:

Computer-Assisted Semen Analysis (CASA) is crucial for consistent and accurate semen image analysis.
Developing and validating CASA algorithms requires reliable methods for simulating diverse semen sample conditions.
Existing CASA algorithm testing relies on real-world samples, which can be variable and difficult to control.

Purpose of the Study:

To present novel simulation models for human sperm cells and their swimming behaviors in 2D images.
To enable the quantitative assessment and comparison of various sperm cell segmentation, localization, and tracking algorithms.
To validate simulation findings using real human semen samples.

Main Methods:

Developed simulation models for sperm cell images, incorporating four distinct swimming patterns (linear, circular, hyperactive, immotile).
Utilized simulated images to test and evaluate segmentation and localization algorithms using precision, recall, and Optimal Subpattern Assignment (OSPA).
Applied simulated data to assess sperm cell tracking algorithms (NN, GNN, PDAF, JPDAF) using Multi-Object Tracking Precision (MOTP) and Accuracy (MOTA).

Main Results:

Simulation models accurately replicate key features of sperm cell images and motion observed in real samples.
Performance metrics from simulations correlated well with validation tests on real semen images for segmentation and localization.
Comparative analysis of tracking algorithms demonstrated the utility of simulations in evaluating algorithm effectiveness under controlled conditions.

Conclusions:

Simulation models offer an objective and efficient platform for assessing the performance of CASA algorithms.
The developed simulation tool facilitates the testing of algorithms across a wide range of parameters and scenarios.
This approach accelerates the advancement of reliable and accurate computer-assisted semen analysis techniques.