# A Survey of Semen Quality Evaluation in Microscopic Videos Using Computer Assisted Sperm Analysis

arXiv: https://arxiv.org/abs/2202.07820 · HTML: https://mlsanity.com/paper/2202.07820/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 71 references that are in the map.
- First posted on arXiv 16 Feb 2022.

## Builds on (most cited first, 3 of 3)

- [Gastric histopathology image segmentation using a hierarchical conditional random field](https://mlsanity.com/paper/2003.01302/) (2020)
- [A Multi-scale CNN-CRF Framework for Environmental Microorganism Image Segmentation](https://mlsanity.com/paper/2003.03744/) (2020)
- [A new Level-set based Protocol for Accurate Bone Segmentation from CT Imaging](https://mlsanity.com/paper/1505.03093/) (2015)

## Built on by (most cited first, 3 of 3)

- [TOD-CNN: An Effective Convolutional Neural Network for Tiny Object Detection in Sperm Videos](https://mlsanity.com/paper/2204.08166/) (2022)
- [ACTIVE: A Deep Model for Sperm and Impurity Detection in Microscopic Videos](https://mlsanity.com/paper/2301.06002/) (2023)
- [Locally Private Online Quantile Regression: Estimation and Inference](https://mlsanity.com/paper/2607.05312/) (2026)

## Abstract (arXiv)

The Computer Assisted Sperm Analysis (CASA) plays a crucial role in male reproductive health diagnosis and Infertility treatment. With the development of the computer industry in recent years, a great of accurate algorithms are proposed. With the assistance of those novel algorithms, it is possible for CASA to achieve a faster and higher quality result. Since image processing is the technical basis of CASA, including pre-processing,feature extraction, target detection and tracking, these methods are important technical steps in dealing with CASA. The various works related to Computer Assisted Sperm Analysis methods in the last 30 years (since 1988) are comprehensively introduced and analysed in this survey. To facilitate understanding, the methods involved are analysed in the sequence of general steps in sperm analysis. In other words, the methods related to sperm detection (localization) are first analysed, and then the methods of sperm tracking are analysed. Beside this, we analyse and prospect the present situation and future of CASA. According to our work, the feasible for applying in sperm microscopic video of methods mentioned in this review is explained. Moreover, existing challenges of object detection and tracking in microscope video are potential to be solved inspired by this survey.