scholarly journals Automatic evaluation of human oocyte developmental potential from microscopy images

2021 ◽  
Author(s):  
Denis Baručić ◽  
Jan Kybic ◽  
Olga Teplá ◽  
Zinovij Topurko ◽  
Irena Kratochvílová
Keyword(s):  
Human Oocyte ◽  
Automatic Evaluation ◽  
Developmental Potential ◽  
Microscopy Images

scholarly journals Human oocyte developmental potential is predicted by mechanical properties within hours after fertilization

2016 ◽  
Vol 7 (1) ◽  
Author(s):  
Livia Z. Yanez ◽  
Jinnuo Han ◽  
Barry B. Behr ◽  
Renee A. Reijo Pera ◽  
David B. Camarillo
Keyword(s):  
Mechanical Properties ◽  
Human Oocyte ◽  
Developmental Potential

MicroRNA-21 as a regulator of human cumulus cell viability and its potential influence on the developmental potential of the oocyte

2020 ◽  
Vol 103 (1) ◽  
pp. 94-103 ◽  
Author(s):  
Alison F Bartolucci ◽  
Tracy Uliasz ◽  
John J Peluso
Keyword(s):  
Cumulus Cell ◽  
Cumulus Cells ◽  
Human Oocyte ◽  
Blastocyst Formation ◽  
Ribonuclease Iii ◽  
Developmental Potential ◽  
Mature Microrna ◽  
Survival Pathway ◽  
Growth Differentiation Factor 9

Abstract MicroRNA-21 is expressed in bovine, murine, and human cumulus cells with its expression in murine and bovine cumulus cells correlated with oocyte developmental potential. The aim of this study was to assess the relationship between cumulus cell MIR-21 and human oocyte developmental potential. These studies revealed that both the immature and mature forms of MicroRNA-21 (MIR-21-5p) were elevated in cumulus cells of oocytes that developed into blastocysts compared to cumulus cells of oocytes that arrested prior to blastocyst formation. This increase in MicroRNA-21 was observed regardless of whether the oocytes developed into euploid or aneuploid blastocysts. Moreover, MIR-21-5p levels in cumulus cells surrounding oocytes that either failed to mature or matured to metaphase II but failed to fertilize, were ≈50% less than the MIR-21-5p levels associated with oocytes that arrested prior to blastocyst formation. Why cumulus cells associated with oocytes of reduced developmental potential expressed less MIR-21-5p is unknown. It is unlikely due to reduced expression of either the receptors of growth differentiation factor 9 or rosha Ribonuclease III (DROSHA) and Dicer Ribonuclease III (DICER) which sequentially promote the conversion of immature forms of MicroRNA-21 to mature MicroRNA-21. Furthermore, cultured cumulus cells treated with a MIR-21-5p inhibitor had an increase in apoptosis and a corresponding increase in the expression of PTEN, a gene known to inhibit the AKT-dependent survival pathway in cumulus cells. These studies provide evidence for a role of MicroRNA-21 in human cumulus cells that influences the developmental potential of human oocytes.

scholarly journals Semantic segmentation of human oocyte images using deep neural networks

2021 ◽  
Vol 20 (1) ◽  
Author(s):  
Anna Targosz ◽  
Piotr Przystałka ◽  
Ryszard Wiaderkiewicz ◽  
Grzegorz Mrugacz
Keyword(s):  
Neural Networks ◽  
Deep Neural Networks ◽  
Semantic Segmentation ◽  
Machine Learning Techniques ◽  
In Vitro Fertilisation ◽  
Human Oocyte ◽  
Automatic Assessment ◽  
Neural Models ◽  
Developmental Potential

AbstractBackgroundInfertility is a significant problem of humanity. In vitro fertilisation is one of the most effective and frequently applied ART methods. The effectiveness IVF depends on the assessment and selection of gametes and embryo with the highest developmental potential. The subjective nature of morphological assessment of oocytes and embryos is still one of the main reasons for seeking effective and objective methods for assessing quality in automatic manner. The most promising methods to automatic classification of oocytes and embryos are based on image analysis aided by machine learning techniques. The special attention is paid on deep neural networks that can be used as classifiers solving the problem of automatic assessment of the oocytes/embryos.MethodsThis paper deals with semantic segmentation of human oocyte images using deep neural networks in order to develop new version of the predefined neural networks. Deep semantic oocyte segmentation networks can be seen as medically oriented predefined networks understanding the content of the image. The research presented in the paper is focused on the performance comparison of different types of convolutional neural networks for semantic oocyte segmentation. In the case study, the merits and limitations of the selected deep neural networks are analysed.Results71 deep neural models were analysed. The best score was obtained for one of the variants of DeepLab-v3-ResNet-18 model, when the training accuracy (Acc) reached about 85% for training patterns and 79% for validation ones. The weighted intersection over union (wIoU) and global accuracy (gAcc) for test patterns were calculated, as well. The obtained values of these quality measures were 0,897 and 0.93, respectively.ConclusionThe obtained results prove that the proposed approach can be applied to create deep neural models for semantic oocyte segmentation with the high accuracy guaranteeing their usage as the predefined networks in other tasks.

Specimen Collection Effect in Scanning Electron Microscopy Images

1972 ◽  
Vol 30 ◽  
pp. 448-449
Author(s):  
J. Temple Black ◽  
Jose Guerrero
Keyword(s):  
Surface Morphology ◽  
Secondary Electron Emission ◽  
Secondary Electrons ◽  
Charge Effects ◽  
Material Density ◽  
Specimen Collection ◽  
The Subject ◽  
Curved Paths ◽  
Subject Material ◽  
Microscopy Images

In the SEM, contrast in the image is the result of variations in the volume secondary electron emission and backscatter emission which reaches the detector and serves to intensity modulate the signal for the CRT's. This emission is a function of the accelerating potential, material density, chemistry, crystallography, local charge effects, surface morphology and especially the angle of the incident electron beam with the particular surface site. Aside from the influence of object inclination, the surface morphology is the most important feature In producing contrast. “Specimen collection“ is the name given the shielding of the collector by adjacent parts of the specimen, producing much image contrast. This type of contrast can occur for both secondary and backscatter electrons even though the secondary electrons take curved paths to the detector-collector.Figure 1 demonstrates, in a unique and striking fashion, the specimen collection effect. The subject material here is Armco Iron, 99.85% purity, which was spark machined.

Scanning ion microscopy images

1987 ◽  
Vol 45 ◽  
pp. 180-181
Author(s):  
R. Levi-Setti ◽  
J.M. Chabala ◽  
Y.L. Wang
Keyword(s):  
Metal Ion ◽  
Secondary Emission ◽  
Scanning Method ◽  
Ion Channeling ◽  
Secondary Ions ◽  
High Resolution Images ◽  
Ion Microscopy ◽  
Z Contrast ◽  
Focused Beams ◽  
Microscopy Images

Finely focused beams extracted from liquid metal ion sources (LMIS) provide a wealth of secondary signals which can be exploited to create high resolution images by the scanning method. The images of scanning ion microscopy (SIM) encompass a variety of contrast mechanisms which we classify into two broad categories: a) Emission contrast and b) Analytical contrast.Emission contrast refers to those mechanisms inherent to the emission of secondaries by solids under ion bombardment. The contrast-carrying signals consist of ion-induced secondary electrons (ISE) and secondary ions (ISI). Both signals exhibit i) topographic emission contrast due to the existence of differential geometric emission and collection effects, ii) crystallographic emission contrast, due to primary ion channeling phenomena and differential oxidation of crystalline surfaces, iii) chemical emission or Z-contrast, related to the dependence of the secondary emission yields on the Z and surface chemical state of the target.

A Transmission Electron Microscopical Investigation of Phase Transformations in TiNi

1980 ◽  
Vol 38 ◽  
pp. 340-341
Author(s):  
P. Moine ◽  
G. M. Michal ◽  
R. Sinclair
Keyword(s):  
Electron Microscopy ◽  
Phase Transformations ◽  
Applied Stress ◽  
Structural Changes ◽  
Microscopical Investigation ◽  
Temperature Range ◽  
Transmission Electron ◽  
Electron Microscopical ◽  
Diffraction Effects ◽  
Microscopy Images

Premartensitic effects in near equiatomic TiNi have been pointed out by several authors(1-5). These include anomalous contrast in electron microscopy images (mottling, striations, etc. ),diffraction effects(diffuse streaks, extra reflections, etc.), a resistivity peak above Ms (temperature at which a perceptible amount of martensite is formed without applied stress). However the structural changes occuring in this temperature range are not well understood. The purpose of this study is to clarify these phenomena.

Sign in / Sign up

Export Citation Format

Share Document