Automatic Human Sperm Concentrartion in microscopic videos

  Background: Human sperm cell counting analysis is of significant interest to biologists studying sperm function and to medical practitioners evaluating male infertility. Currently the analysis of this assessment is done manually by looking at the sperm samples through a phase-contrast microscope using expert knowledge to do a subjective judgement of the quality. Aims: to eliminate the subjective and error prone of the manual semen analysis and to avoid inter and intra-laboratory inconsistencies in semen analysis test results Methods: In this paper we introduce a technique for human sperm concentration. Its principle is based on the execution of three steps: The first step in unavoidable. It concerns the pretreatment of the human sperm microscopic videos which consists of a conversion of the RGB color space into the YCbCr space, the “Gaussian filtering” and the “discrete wavelet filtering”. The second step is devoted to the segmentation of the image into two classes: spermatozoas and the background. To achieve this, we used an edge detection technique “Sobel Contour detector”. The third step is to separate true sperm from false ones. It uses a machine learning technique of type decision trees that consist on two classes classification based on invariant characteristics that are the dimensions of the bounding ellipse of the spermatozoid head as well as its surface. Results: To test the robustness of our system, we compared our results with those performed manually by andrologists. After results analysis, we can conclude that our system brings a real improvement of precision as well as treatment time which make it might be useful for groups who intend to design new CASA systems. Conclusion: In this study, we designed and implemented a system for automatic concentration assessment based on machine learning method and image processing techniques

Mixture gaussian V2 based microscopic movement detection of human spermatozoa

Healthy and superior sperm is the main requirement for a woman to get pregnant. To find out how the quality of sperm is needed several checks. One of them is a sperm analysis test to see the movement of sperm objects, the analysis is observed using a microscope and calculated manually. The first step in analyzing the scheme is detecting and separating sperm objects. This research is detecting and calculating sperm movements in video data. To detect moving sperm, the background processing of sperm video data is essential for the success of the next process. This research aims to apply and compare some background subtraction algorithms to detect and count moving sperm in microscopic videos of sperm fluid, so we get a background subtraction algorithm that is suitable for the case of sperm detection and sperm count. The research methodology begins with the acquisition of sperm video data. Then, preprocessing using a Gaussian filter, background subtraction, morphological operations that produce foreground masks, and compared with moving sperm ground truth images for validation of the detection results of each background subtraction algorithm. It also shows that the system has been able to detect and count moving sperm. The test results show that the MoG (Mixture of Gaussian) V2 (2 Dimension Variable) algorithm has an f-measure value of 0.9449 and has succeeded in extracting sperm shape close to its original form and is superior compared to other methods. To conclude, the sperm analysis process can be done automatically and efficiently in terms of time

A review of different deep learning techniques for sperm fertility prediction

Sperm morphology analysis (SMA) is a significant factor in diagnosing male infertility. Therefore, healthy sperm detection is of great significance in this process. However, the traditional manual microscopic sperm detection methods have the disadvantages of a long detection cycle, low detection accuracy in large orders, and very complex fertility prediction. Therefore, it is meaningful to apply computer image analysis technology to the field of fertility prediction. Computer image analysis can give high precision and high efficiency in detecting sperm cells. In this article, first, we analyze the existing sperm detection techniques in chronological order, from traditional image processing and machine learning to deep learning methods in segmentation and classification. Then, we analyze and summarize these existing methods and introduce some potential methods, including visual transformers. Finally, the future development direction and challenges of sperm cell detection are discussed. We have summarized 44 related technical papers from 2012 to the present. This review will help researchers have a more comprehensive understanding of the development process, research status, and future trends in the field of fertility prediction and provide a reference for researchers in other fields