Deep Learning-based Automated Rare Sperm Identification from Testes Biopsies

Non-obstructive azoospermia (NOA), the most severe form of male infertility, is currently treated using microsurgical sperm extraction (microTESE) to retrieve sperm cells for in vitro fertilization via intracytoplasmic sperm injection (IVF-ICSI). The success rate of this procedure for NOA patients is currently limited by the ability of andrologists to identify a few rare sperm cells among millions of background testis cells. To improve this success rate, we developed a convolution neural network (CNN) to detect rare sperm from low-resolution microscopy images of microTESE samples. Our CNN uses the U-Net architecture to perform pixel-based classification on image patches from brightfield microscopy, which is followed by morphological analysis to detect individual sperm instances. This CNN is trained using microscopy images of fluorescently labeled sperm, which is fixed to eliminate their motility, and doped into testis biopsies obtained from NOA patients. We initially tested this algorithm using purified sperm samples at different imaging magnifications in order to determine the upper bounds of performance. We then tested this algorithm by doping rare sperm cells into testis biopsy samples from NOA patients and found a sperm detection F1 score of 85.2%. These results demonstrate the potential to use automated microscopy to dramatically increase the amount of testis biopsy tissue that could be comprehensively examined, which greatly increases the chance of finding rare viable sperm, and thereby increases the success rates of IVF-ICSI for couples with NOA.

Multistage Optimization Using a Modified Gaussian Mixture Model in Sperm Motility Tracking

Infertility is a condition whereby pregnancy does not occur despite having unprotected sexual intercourse for at least one year. The main reason could originate from either the male or the female, and sometimes, both contribute to the fertility disorder. For the male, sperm disorder was found to be the most common reason for infertility. In this paper, we proposed male infertility analysis based on automated sperm motility tracking. The proposed method worked in multistages, where the first stage focused on the sperm detection process using an improved Gaussian Mixture Model. A new optimization protocol was proposed to accurately detect the motile sperms prior to the sperm tracking process. Since the optimization protocol was imposed in the proposed system, the sperm tracking and velocity estimation processes are improved. The proposed method attained the highest average accuracy, sensitivity, and specificity of 92.3%, 96.3%, and 72.4%, respectively, when tested on 10 different samples. Our proposed method depicted better sperm detection quality when qualitatively observed as compared to other state-of-the-art techniques.

Computer-Assisted Analysis of Human Semen Concentration and Motility

Computer-assisted semen analysis systems insist on evaluating sperm characteristics. These systems afford capacity to study and evaluate sperm statistical and morphological characteristics such as concentration, morphology, and motility, which have an important role in diagnosis and treatment of male infertility. In this paper, the proposed algorithm allows the assessment of concentration and motility rate of sperms in microscopic videos. First, enhancement process is required because of microscopic images limitations such as low contrast and noises. Then, for true sperm recognition among noise and debris, a hybrid approach is proposed using a combination between segmentation techniques. After, the use of geometric features of the bounding ellipse of the sperm head led to define sperm concentration. Finally, inter-frame difference is applied for motile sperm detection. The proposed method was tested on microscopic videos of human semen; the performance of this method is analyzed in terms of speed, accuracy, and complexity. Obtained results during the experiments are very promising compared with those obtained by the traditional assessment, which is the most widely used and approved in the laboratories.

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.

Counting the Number of Active Spermatozoa Movements Using Improvement Adaptive Background Learning Algorithm

The most important early stage in sperm infertility research is the detection of sperm objects. The success rate in separating sperm objects from semen fluid has an important role for further analysis. This research performed the detection and calculation of human spermatozoa. The detected sperm was the moving sperm in the video data. An improvement of Adaptive Background Learning was applied to detect the moving sperm. The purpose of this method is to improve the performance of Adaptive Background Learning algorithm in background subtraction process to detect and calculate moving sperm on the microscopic video of sperm fluid. This paper also compared several other background subtraction algorithms to conclude the appropriate background subtraction algorithm for sperm detection and sperm counting. The process done in this research was preprocessing using the Gaussian filter. The next was background subtraction process, followed by morphology operation. To test or validate the detection results of any background subtraction algorithm used, the foreground mask results from the morphological operation were compared to the ground truth of moving sperm image. For visualization purposes, every BLOB area (white object in binary image) on the foreground were given a bounding box to the original frame and the number of BLOB objects present in the foreground mask were counted. This shows that the system had been able to detect and calculate moving sperm. Based on the test results, Adaptive Background Learning method had a value of F-measure of 0.9205 and succeeded in extracting sperm shape close to the original form compared to other methods.