Automated Non-Invasive Measurement of Single Sperm's Motility and Morphology

A Stained-Free Sperm Morphology Measurement Method Based on Multi-Target Instance Parsing and Measurement Accuracy Enhancement

Sperm morphology assessment plays a vital role in semen analysis and the diagnosis of male infertility. By quantitatively analyzing the morphological characteristics of the sperm head, midpiece, and tail, it provides essential insights for assisted reproductive technologies (ARTs), such as in vitro fertilization (IVF). However, traditional manual evaluation methods not only rely on staining procedures that can damage the cells but also suffer from strong subjectivity and inconsistent results, underscoring the urgent need for an automated, accurate, and non-invasive method for multi-sperm morphology assessment. To address the limitations of existing techniques, this study proposes a novel method that combines a multi-scale part parsing network with a measurement accuracy enhancement strategy for non-stained sperm morphology analysis. First, a multi-scale part parsing network integrating semantic segmentation and instance segmentation is introduced to achieve instance-level parsing of sperm, enabling precise measurement of morphological parameters for each individual sperm instance. Second, to eliminate measurement errors caused by the reduced resolution of non-stained sperm images, a measurement accuracy enhancement method based on statistical analysis and signal processing is designed. This method employs an interquartile range (IQR) method to exclude outliers, Gaussian filtering to smooth data, and robust correction techniques to extract the maximum morphological features of sperm. Experimental results demonstrate that the proposed multi-scale part parsing network achieves 59.3% APvolp, surpassing the state-of-the-art AIParsing by 9.20%. Compared to evaluations based solely on segmentation results, the integration of the measurement accuracy enhancement strategy significantly reduces measurement errors, with the largest reduction in errors for head, midpiece, and tail measurements reaching up to 35.0%.

Exploring sperm cell motion dynamics: Insights from genetic algorithm-based analysis

Accurate analysis of sperm cell flagellar dynamics plays a crucial role in understanding sperm motility as flagella parameters determine cell behavior in the spatiotemporal domain. In this study, we introduce a novel approach by harnessing Genetic Algorithms (GA) to analyze sperm flagellar motion characteristics and compare the results with the traditional decomposition method based on Fourier analysis. Our analysis focuses on extracting key parameters of the equation approximating flagellar shape, including beating period time, bending amplitude, mean curvature, and wavelength. Additionally, we delve into the extraction of phase constants and initial swimming directions, vital for the comprehensive study of sperm cell pairs and bundling phenomena. One significant advantage of GA over Fourier analysis is its ability to integrate sperm cell motion data, enabling a more comprehensive analysis. In contrast, Fourier analysis neglects sperm cell motion by transitioning to a sperm-centered coordinate system (material system). In our comparative study, GA consistently outperform the Fourier analysis-based method, yielding a remarkable reduction in fitting error of up to 70% and on average by 45%. An in-depth exploration of the sperm cell motion becomes indispensable in a wide range of applications from complexities of reproductive biology and medicine, to developing soft flagellated microrobots.

Artificial Intelligence in Andrology: From Semen Analysis to Image Diagnostics

Artificial intelligence (AI) in medicine has gained a lot of momentum in the last decades and has been applied to various fields of medicine. Advances in computer science, medical informatics, robotics, and the need for personalized medicine have facilitated the role of AI in modern healthcare. Similarly, as in other fields, AI applications, such as machine learning, artificial neural networks, and deep learning, have shown great potential in andrology and reproductive medicine. AI-based tools are poised to become valuable assets with abilities to support and aid in diagnosing and treating male infertility, and in improving the accuracy of patient care. These automated, AI-based predictions may offer consistency and efficiency in terms of time and cost in infertility research and clinical management. In andrology and reproductive medicine, AI has been used for objective sperm, oocyte, and embryo selection, prediction of surgical outcomes, cost-effective assessment, development of robotic surgery, and clinical decision-making systems. In the future, better integration and implementation of AI into medicine will undoubtedly lead to pioneering evidence-based breakthroughs and the reshaping of andrology and reproductive medicine.

Ensembling noisy segmentation masks of blurred sperm images

BACKGROUND

Sperm tail morphology and motility have been demonstrated to be important factors in determining sperm quality for in vitro fertilization. However, many existing computer-aided sperm analysis systems leave the sperm tail out of the analysis, as detecting a few tail pixels is challenging. Moreover, some publicly available datasets for classifying morphological defects contain images limited only to the sperm head. This study focuses on the segmentation of full sperm, which consists of the head and tail parts, and appear alone and in groups.

METHODS

We re-purpose the Feature Pyramid Network to ensemble an input image with multiple masks from state-of-the-art segmentation algorithms using a scale-specific cross-attention module. We normalize homogeneous backgrounds for improved training. The low field depth of microscopes blurs the images, easily confusing human raters in discerning minuscule sperm from large backgrounds. We thus propose evaluation protocols for scoring segmentation models trained on imbalanced data and noisy ground truth.

RESULTS

The neural ensembling of noisy segmentation masks outperforms all single, state-of-the-art segmentation algorithms in full sperm segmentation. Human raters agree more on the head than tail masks. The algorithms also segment the head better than the tail.

CONCLUSIONS

The extensive evaluation of state-of-the-art segmentation algorithms shows that full sperm segmentation is challenging. We release the SegSperm dataset of images from Intracytoplasmic Sperm Injection procedures to spur further progress on full sperm segmentation with noisy and imbalanced ground truth. The dataset is publicly available at https://doi.org/10.34808/6wm7-1159.

Canine Semen Evaluation and Processing

Advances in canine semen evaluation have progressed over time in fits and spurts, interspersed with long periods of relative inactivity. Despite exciting advances in the semen analysis, clinical canine theriogenology has been in a period of relative inactivity for a number of decades since initial advances in canine semen freezing in the mid 20th century. This review describes ways that the clinical practice of canine semen evaluation should improve, given the state of current knowledge.

Fast Noninvasive Morphometric Characterization of Free Human Sperms Using Deep Learning

The selection of high-quality sperms is critical to intracytoplasmic sperm injection, which accounts for 70–80% of in vitro fertilization (IVF) treatments. So far, sperm screening is usually performed manually by clinicians. However, the performance of manual screening is limited in its objectivity, consistency, and efficiency. To overcome these limitations, we have developed a fast and noninvasive three-stage method to characterize morphology of freely swimming human sperms in bright-field microscopy images using deep learning models. Specifically, we use an object detection model to identify sperm heads, a classification model to select in-focus images, and a segmentation model to extract geometry of sperm heads and vacuoles. The models achieve an F1-score of 0.951 in sperm head detection, a z-position estimation error within ±1.5 μm in in-focus image selection, and a Dice score of 0.948 in sperm head segmentation, respectively. Customized lightweight architectures are used for the models to achieve real-time analysis of 200 frames per second. Comprehensive morphological parameters are calculated from sperm head geometry extracted by image segmentation. Overall, our method provides a reliable and efficient tool to assist clinicians in selecting high-quality sperms for successful IVF. It also demonstrates the effectiveness of deep learning in real-time analysis of live bright-field microscopy images.

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.

Advances in sperm analysis: techniques, discoveries and applications

Infertility affects one in six couples worldwide, and fertility continues to deteriorate globally, partly owing to a decline in semen quality. Sperm analysis has a central role in diagnosing and treating male factor infertility. Many emerging techniques, such as digital holography, super-resolution microscopy and next-generation sequencing, have been developed that enable improved analysis of sperm motility, morphology and genetics to help overcome limitations in accuracy and consistency, and improve sperm selection for infertility treatment. These techniques have also improved our understanding of fundamental sperm physiology by enabling discoveries in sperm behaviour and molecular structures. Further progress in sperm analysis and integrating these techniques into laboratories and clinics requires multidisciplinary collaboration, which will increase discovery and improve clinical outcomes.

Quantitative selection of single human sperm with high DNA integrity for intracytoplasmic sperm injection

OBJECTIVE

To study at the single-cell level whether a sperm's motility and morphology parameters reflect its DNA integrity, and to establish a set of quantitative criteria for selecting single sperm with high DNA integrity.

DESIGN

Prospective study.

SETTING

In vitro fertilization center and university laboratories.

PATIENT(S)

Male patients undergoing infertility treatments.

INTERVENTION(S)

None.

MAIN OUTCOME MEASURE(S)

The motility and morphology parameters of each sperm were measured with the use of computer vision algorithms. The sperm was then aspirated and transferred for DNA fragmentation measurement by single-cell gel electrophoresis (comet assay).

RESULT(S)

We adapted the World Health Organization criteria, which were originally defined for semen analysis, and established a set of quantitative criteria for single-sperm selection in intracytoplasmic sperm injection. Sperm satisfying the criteria had significantly lower DNA fragmentation levels than the sample population. Both normal motility and normal morphology were required for a sperm to have low DNA fragmentation. The quantitative criteria were integrated into a software program for sperm selection. In blind tests in which our software and three embryologists selected sperm from the same patient samples, our software outperformed the embryologists and selected sperm with the highest DNA integrity.

CONCLUSION(S)

At the single-cell level, a sperm's motility and morphology parameters reflect its DNA integrity. The developed technique and criteria hold the potential to mitigate the risk factor of sperm DNA fragmentation in intracytoplasmic sperm injection.

Machine learning for sperm selection

Infertility rates and the number of couples seeking fertility care have increased worldwide over the past few decades. Over 2.5 million cycles of assisted reproductive technologies are being performed globally every year, but the success rate has remained at ~33%. Machine learning, an automated method of data analysis based on patterns and inference, is increasingly being deployed within the health-care sector to improve diagnostics and therapeutics. This technique is already aiding embryo selection in some fertility clinics, and has also been applied in research laboratories to improve sperm analysis and selection. Tremendous opportunities exist for machine learning to advance male fertility treatments. The fundamental challenge of sperm selection - selecting the most promising candidate from 10⁸ gametes - presents a challenge that is uniquely well-suited to the high-throughput capabilities of machine learning algorithms paired with modern data processing capabilities.

Automated motility and morphology measurement of live spermatozoa

BACKGROUND

Automated sperm analysis has wide applications in infertility diagnosis. Existing systems are not able to measure sperm count and both motility and morphology of individual live spermatozoa. Morphology measurement requires invasive staining, making the spermatozoa after morphology measurement not applicable to infertility treatment.

OBJECTIVE

To evaluate the reproducibility and reliability of automated measurement of individual live sperm's motility and morphology.

MATERIALS AND METHODS

Fresh semen samples were obtained from twenty male partners attending for fertility investigations. The system firstly measured motility for all the spermatozoa within the field of view under a low magnification (20×), then a spermatozoa of interest is selected by the user and automatically relocated by the system after switching to a high magnification (100×) for morphology measurement. Reproducibility of sperm measurements was evaluated by intraclass correlation coefficients on consecutive measurement. Reliability of motility and morphology measurement was evaluated by tracking error rate and limits of agreement, respectively, with manual measurement as benchmark.

RESULTS

Measurement of all motility and morphology parameters had intraclass correlation coefficients higher than 0.94. Sperm motility measurement had a tracking error rate of 2.1%. Limit of agreement analysis indicated that automated measurement and manual measurement of sperm morphology were interchangeable. Automated measurement of all morphology parameters was not statistically different from manual measurement, as confirmed by the paired sample t test.

DISCUSSION

Automated motility and morphology measurement of single sperm revealed high reproducibility and reliability. The system also achieved a high efficiency for motility and morphology measurement. In addition to the intracytoplasmic sperm injection (ICSI) samples with polyvinylpyrrolidone (PVP), the developed sperm measurement technique is also effective for analyzing semen and washed samples. The system provides a valuable tool for quantitative measurement and selection of single spermatozoa for ICSI. It can also be used for sperm motility and morphology analysis in andrology laboratories.

Deep learning-based selection of human sperm with high DNA integrity

Despite the importance of sperm DNA to human reproduction, currently no method exists to assess individual sperm DNA quality prior to clinical selection. Traditionally, skilled clinicians select sperm based on a variety of morphological and motility criteria, but without direct knowledge of their DNA cargo. Here, we show how a deep convolutional neural network can be trained on a collection of ~1000 sperm cells of known DNA quality, to predict DNA quality from brightfield images alone. Our results demonstrate moderate correlation (bivariate correlation ~0.43) between a sperm cell image and DNA quality and the ability to identify higher DNA integrity cells relative to the median. This deep learning selection process is directly compatible with current, manual microscopy-based sperm selection and could assist clinicians, by providing rapid DNA quality predictions (under 10 ms per cell) and sperm selection within the 86th percentile from a given sample.