Convolutional neural networks for segmentation and object detection of human semen

We compare a set of convolutional neural network (CNN) architectures for the task of segmenting and detecting human sperm cells in an image taken from a semen sample. In contrast to previous work, samples are not stained or washed to allow for full sperm quality analysis, making analysis harder due to clutter. Our results indicate that training on full images is superior to training on patches when class-skew is properly handled. Full image training including up-sampling during training proves to be beneficial in deep CNNs for pixel wise accuracy and detection performance. Predicted sperm cells are found by using connected components on the CNN predictions. We investigate optimization of a threshold parameter on the size of detected components. Our best network achieves 93.87% precision and 91.89% recall on our test dataset after thresholding outperforming a classical mage analysis approach.Comment: Submitted for Scandinavian Conference on Image Analysis 201

Sperm cell segmentation in digital micrographs based on convolutional neural networks using u-net architecture

Human infertility is considered a serious disease of the the reproductive system that affects more than 10% of couples worldwide,and more than 30% of reported cases are related to men. The crucial step in evaluating male in fertility is a semen analysis, highly dependent on sperm morphology. However,this analysis is done at the laboratory manually and depends mainly on the doctor’s experience. Besides,it is laborious, and there is also a high degree of interlaboratory variability in the results. This article proposes applying a specialized convolutional neural network architecture (U-Net),which focuses on the segmentation of sperm cells in micrographs to overcome these problems.The results showed high scores for the model segmentation metrics such as precisión (93%), IoU score (86%),and DICE score of 93%. Moreover,we can conclude that U-net architecture turned out to be a good option to carry out the segmentation of sperm cells

Automated Sperm Assessment Framework and Neural Network Specialized for Sperm Video Recognition

Infertility is a global health problem, and an increasing number of couples are seeking medical assistance to achieve reproduction, at least half of which are caused by men. The success rate of assisted reproductive technologies depends on sperm assessment, in which experts determine whether sperm can be used for reproduction based on morphology and motility of sperm. Previous sperm assessment studies with deep learning have used datasets comprising images that include only sperm heads, which cannot consider motility and other morphologies of sperm. Furthermore, the labels of the dataset are one-hot, which provides insufficient support for experts, because assessment results are inconsistent between experts, and they have no absolute answer. Therefore, we constructed the video dataset for sperm assessment whose videos include sperm head as well as neck and tail, and its labels were annotated with soft-label. Furthermore, we proposed the sperm assessment framework and the neural network, RoSTFine, for sperm video recognition. Experimental results showed that RoSTFine could improve the sperm assessment performances compared to existing video recognition models and focus strongly on important sperm parts (i.e., head and neck).Comment: Accepted at Winter Conference on Applications of Computer Vision (WACV) 202

Implementation of novel statistical procedures and other advanced approaches to improve analysis of CASA data

P. 860-866Computer-aided sperm analysis (CASA) produces a wealth of data that is frequently ignored. The use of multiparametric statistical methods can help explore these datasets, unveiling the subpopulation structure of sperm samples. In this review we analyse the significance of the internal heterogeneity of sperm samples and its relevance. We also provide a brief description of the statistical tools used for extracting sperm subpopulations from the datasets, namely unsupervised clustering (with non-hierarchical, hierarchical and two-step methods) and the most advanced supervised methods, based on machine learning. The former method has allowed exploration of subpopulation patterns in many species, whereas the latter offering further possibilities, especially considering functional studies and the practical use of subpopulation analysis. We also consider novel approaches, such as the use of geometric morphometrics or imaging flow cytometry. Finally, although the data provided by CASA systems provides valuable information on sperm samples by applying clustering analyses, there are several caveats. Protocols for capturing and analysing motility or morphometry should be standardised and adapted to each experiment, and the algorithms should be open in order to allow comparison of results between laboratories. Moreover, we must be aware of new technology that could change the paradigm for studying sperm motility and morphology.S

Impact of Nanotechnology-Based Semen Purification on Reproduction of Gilts and Developmental Performance of Offspring

Semen contain a heterogeneous population of viable and non-viable (damaged) spermatozoa. Proportions of non-viable spermatozoa interfere with male fertility, with available techniques unable to selectively remove prior to breeding. Nanobiotechnology may allow removal, enriching semen with high quality spermatozoa for improved productivity. Here, we applied double nanopurification with boar semen using functionalized magnetic nanoparticles. Non-viable and viable spermatozoa were magnetically separated and verified through various microscopy imaging. Viable (nanopurified) spermatozoa showed no additional damages. Nanopurification did not interfere with sperm motility and viability, with beneficial effects on motion parameters. Nanopurified spermatozoa maintained fertility following insemination, with resulting offspring indicating no impaired growth or health performance. Pork quality was unaffected showing comparable characteristics to the control. In summary, the use of magnetic nanopurification in boar spermatozoa showed sperm viability and fertility improvements with successful offspring performance. This study shows promise for large-scale commercial applications to enhance male fertility and offspring performance

Gold-standard of HER2 breast cancer biopsies using supervised learning based on multiple pathologist annotations

Breast cancer is one of the most common cancer in women around the world. For diagnosis, pathologists evaluate biomarkers such as HER2 protein using immunohistochemistry over tissue extracted by a biopsy. Through microscopic inspection, this assessment estimates the intensity and integrity of the membrane cells' staining and scores the sample as 0, 1+, 2+, or 3+: a subjective decision that depends on the interpretation of the pathologist. This paper presents the preliminary data analysis of the annotations of three pathologists over the same set of samples obtained using 20x magnification and including $1,252$ non-overlapping biopsy patches. We evaluate the intra- and inter-expert variability achieving substantial and moderate agreement, respectively, according to Fleiss' Kappa coefficient, as a previous stage towards a generation of a HER2 breast cancer biopsy gold-standard using supervised learning from multiple pathologist annotations

Sperm activation in Nile tilapia Oreochromis niloticus and the effects of environmentally relevant pollutants on sperm fitness

In externally fertilizing fishes, multiple factors of the spawning environment may affect the sperm viability, and thus the fertilization rate. In this thesis, the sperm activation effect of osmolality of non-electrolytes and electrolytes activation media, pH and ion channel inhibitors on Nile tilapia, Oreochromis niloticus, and the effect of environmentally relevant pollutants (cadmium, malathion and rotenone) on sperm fitness (motility and morphology) were investigated. Seminal fluid samples collected from male fishes (200-250g) were subjected to activation treatments, then analyzed for sperm motility using motility score, and motility variables using Hobson sperm tracker for straight line velocity (VSL), beat cross frequency (BCF) and percentage of motile cells (MOT). For the ion channel inhibitors and pollutants, the effect on sperm motility variables of VSL, VCL (curvilinear velocity) and LIN (linearity) were determined. Multivariate analysis was also carried out to determine the effects of ion channel inhibitors and pollutants on sperm subpopulations. The effects of pollutants on sperm morphology were observed using microscopy techniques, namely, scanning electron microscopy (SEM) and transmission electron microscopy (TEM). Sperm motility was initiated when the sperm were exposed to hypoosmotic electrolytes and non-electrolytes solution. We also found that sperm show optimal activity at pH range of 6-8 which depicts that the effect of pH on sperm motility is negligible. Lanthanum (calcium channel blocker) and flunarizine (sodium-calcium exchanger pump blocker) were found to inhibit sperm motility at 25 and 5 µM, respectively, suggesting that both ion channels play a significant role in sperm activation in O. niloticus. In contrast amiloride, ouabain and quinine showed no effects on activation, indicating that epithelial sodium channels, sodium-potassium ATPase and voltage gated potassium channels respectively are unlikely to have major roles in sperm activation or motility. The spermatozoa of Oreochromis niloticus were uniflagellate with clearly differentiated oval-shaped head, midpiece and flagellum. Sperm exposed to hypoosmotic shock showed swelling of the midpiece and sleeve structure. The pollutants showed dose- and time-dependent effect on sperm motility of the fast linear sperm subpopulation. Sperm morphology was not affected. Sperm motility was inhibited at 0.44, 0.03 and 0.063 µM, cadmium, malathion and rotenone respectively. Both cadmium and malathion exerted effects very quickly after exposure. The effect of cadmium, which can exert toxicity by calcium antagonism, is consistent with the effects of calcium channel blockes and further supports an important role for calcium in sperm activation and motility. Malathion had effects at relatively low, environmentally relevant concentrations, suggesting the presence of functionally important acetylcholinesterase activity in sperm, and also the presence of activation cytochrome P450 activity. Rotenone, a well known mitochondrial poison, affected motility only after 15 min of pretreatment. The alteration of sperm trajectories in fast linear spermatozoa subpopulation by pollutants at submicromolar concentrations as demonstrated in our study implies potentially serious consequences for fish populations in polluted environments. Furthermore the results indicate that fish sperm motility as assessed by CASA could be an ecologically relevant, sensitive, and ethically acceptable method for toxicity testing in environmental risk assessment

Quantification and identification of sperm subpopulations using computer-aided sperm analysis and species-specific cut-off values for swimming speed

Motility is an essential characteristic of all fl agellated spermatozoa and assessment of this parameter is one criterion for most semen or sperm evaluations. Computer-aided sperm analysis (CASA) can be used to measure sperm motility more objectively and accurately than manual methods, provided that analysis techniques are standardized. Previous studies have shown that evaluation of sperm subpopulations is more important than analyzing the total motile sperm population alone. We developed a quantitative method to determine cut-off values for swimming speed to identify three sperm subpopulations. We used the Sperm Class Analyzer ® (SCA) CASA system to assess the total percentage of motile spermatozoa in a sperm preparation as well as the percentages of rapid, medium and slow swimming spermatozoa for six mammalian species. Curvilinear velocity (VCL) cut-off values were adjusted manually for each species to include 80% rapid, 15% medium and 5% slow swimming spermatozoa. Our results indicate that the same VCL intervals cannot be used for all species to classify spermatozoa according to swimming speed. After VCL intervals were adjusted for each species, three unique sperm subpopulations could be identifi ed. The effects of medical treatments on sperm motility become apparent in changes in the distribution of spermatozoa among the three swimming speed classes.Web of Scienc