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

Implementation of Clinical Assisted Reproduction Technologies for the Improvement of in vitro Production of Porcine Embryos: From IVF Clinic to Pig Farm

The world population is predicted to rise from 7 to 9 billion in the next 30 years, and per capita, meat consumption is predicted to increase by 20% at this time. This places a demand on current food producers globally (particularly pork producers as 40% of global meat consumption is pig meat) that is not sustainable unless sufficient innovations are implemented. Livestock production also contributes 18% of the earth's global warming, and this is also set to increase. Solving these problems necessitates producing increased amounts of meat from fewer animals in a shorter amount of time. UK companies lead the world in developing livestock with superior genetic traits that drive increased productivity through greater feed conversion efficiencies, improved disease resistance, and higher fertility. Disseminating and applying these advances into herds around the world, however, presents unique problems. That is, for female line genetics, (male line genetics can be disseminated via sperm samples) producers are left with no other choice but to transport live animals for establishing nucleus farms overseas (e.g. In East and Southeast Asia). This can be expensive; energy is consuming, environmentally unfriendly, and carries important animal welfare and disease transmission concerns. One possible solution is to preserve and transports superior genetics in the form of preimplantation embryos (preferably pre-genotyped for sex and desirable production trait). To date, however, pig IVF and production (henceforth termed "IVP") has not been successfully implemented. The purpose of this thesis was to contribute to an ongoing effort to improve pig IVP through fundamental studies of porcine reproduction. Specifically, the work focussed on boar sperm production and on the human system (IVF clinic data) to provide clues as to the likely effects of embryo biopsy - an essential precursor to genotyping a preimplantation embryo as follows: The first aim was to produce a working classification system for boar sperm morphology and test the hypothesis that there are differences between high quality and poor quality boars. Some hitherto unreported features of sperm morphology were established as significantly different in the poor-quality boar seen group. The second was to assess the effects of stimulants (e.g. caffeine and adenosine) on capacitation and fertilization rates and ask whether there was a correlation between capacitation and fertilization. Here, the utility of caffeine was established, and correlations were observed between sperm morphology and capacitation rates. The third aim involved establishing whether novel markers of correct sperm chromatin packaging (CMA3 stain, nuclear organization, sperm aneuploidy) were indicative of reduced fertility in boars. Here a significant association between the poor-quality boars and level of CMA3 staining was observed indicating that this test may be implemented in the future as a means of identifying poor quality boars. No significant association with nuclear organization nor sperm aneuploidy was observed, however. Finally, attention turned to human IVF data to test the hypothesis that embryo biopsy adversely affected subsequent embryo development. Using state of the art time lapse imaging no evidence was found to indicate that biopsy had an adverse effect in humans suggesting that, if performed correctly, this may also be the case in pigs. Taken together, the results provide evidence for the potential of significant advances in pig IVP by adapting protocols already commonplace in humans. Indeed, during the project, and in part because of it, IVP success rates in the laboratory increased dramatically

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