Eliminating the effect of pathomorphologically formed sperm on resulting gravidity using the intracytoplasmic sperm injection method

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.

The influence of semen quality on male pronucleus demethylation process during ICSI cycle

There is growing evidence that the spermatozoon's epigenetic structure is of the utmost importance in the health of the future embryo. Following fertilization, sperm chromatin undergoes epigenetic reprogramming including DNA demethylation and remethylation, which resets gene expression. In some infertile patients, it is inevitable that sperm cells that are not within the range of normal human sperm parameters will be used for intracytoplasmic sperm injection. Understanding the relationship between the human sperm parameters and male pronucleus DNA demethylation seems necessary. We hypothesized that demethylation of the male pronucleus might be altered in zygotes conceived from a spermatozoa obtained from a sample exhibiting an abnormal semen analysis profile. To test the hypothesis, sperm cells from normal and abnormal human semen samples were injected into mouse oocytes. A group of cultured zygotes was fixed before the onset of DNA demethylation and the other group was fixed after DNA demethylation. Both groups were then labeled with a 5 methylcytosine antibody and the level of pronuclei methylation was detected as a function of fluorescent intensity. The level of demethylation was then determined as the difference between 5 methylcytosine fluorescent intensity before and after DNA demethylation. A negative correlation (p<0.05) was observed between sperm motility, morphology, percentage of head defects, protamine deficiency, and DNA demethylation level. However, no correlation was found between the demethylation level and sperm count. In conclusion, these observations suggest that demethylation is altered in the male pronucleus when low quality sperm samples are used.

An efficient method for automatic morphological abnormality detection from human sperm images

BACKGROUND AND OBJECTIVE

Sperm morphology analysis (SMA) is an important factor in the diagnosis of human male infertility. This study presents an automatic algorithm for sperm morphology analysis (to detect malformation) using images of human sperm cells.

METHODS

The SMA method was used to detect and analyze different parts of the human sperm. First of all, SMA removes the image noises and enhances the contrast of the image to a great extent. Then it recognizes the different parts of sperm (e.g., head, tail) and analyzes the size and shape of each part. Finally, the algorithm classifies each sperm as normal or abnormal. Malformations in the head, midpiece, and tail of a sperm, can be detected by the SMA method. In contrast to other similar methods, the SMA method can work with low resolution and non-stained images. Furthermore, an image collection created for the SMA, has also been described in this study. This benchmark consists of 1457 sperm images from 235 patients, and is known as human sperm morphology analysis dataset (HSMA-DS).

RESULTS

The proposed algorithm was tested on HSMA-DS. The experimental results show the high ability of SMA to detect morphological deformities from sperm images. In this study, the SMA algorithm produced above 90% accuracy in sperm abnormality detection task. Another advantage of the proposed method is its low computation time (that is, less than 9s), as such, the expert can quickly decide to choose the analyzed sperm or select another one.

CONCLUSIONS

Automatic and fast analysis of human sperm morphology can be useful during intracytoplasmic sperm injection for helping embryologists to select the best sperm in real time.