Morphokinetic analysis of human embryo development and its relationship to the female age: a retrospective time-lapse imaging study

2017 ◽  
Vol 63 (8) ◽  
pp. 84 ◽  
Author(s):  
N. Akhter ◽  
M. Shahab
Keyword(s):  
Embryo Development ◽  
Human Embryo ◽  
Imaging Study ◽  
Time Lapse ◽  
Female Age ◽  
Human Embryo Development ◽  
Time Lapse Imaging

The evaluation of human embryo development in IVF/ICSI fertilized oocytes: a time-lapse study

2014 ◽  
Vol 102 (3) ◽  
pp. e207
Author(s):  
H.J. Kim ◽  
H.J. Yoon ◽  
J.M. Jang ◽  
W.D. Lee ◽  
S.H. Yoon ◽  
...  
Keyword(s):  
Embryo Development ◽  
Human Embryo ◽  
Time Lapse ◽  
Human Embryo Development

scholarly journals Towards the automation of early-stage human embryo development detection

2019 ◽  
Vol 18 (1) ◽  
Author(s):  
Vidas Raudonis ◽  
Agne Paulauskaite-Taraseviciene ◽  
Kristina Sutiene ◽  
Domas Jonaitis
Keyword(s):  
Deep Learning ◽  
Assisted Reproductive Technology ◽  
Embryo Development ◽  
Human Embryo ◽  
Early Stage ◽  
Significant Proportion ◽  
Time Lapse ◽  
Reproductive Technology ◽  
In Vitro Fertilisation ◽  
Human Embryo Development

Abstract Background Infertility and subfertility affect a significant proportion of humanity. Assisted reproductive technology has been proven capable of alleviating infertility issues. In vitro fertilisation is one such option whose success is highly dependent on the selection of a high-quality embryo for transfer. This is typically done manually by analysing embryos under a microscope. However, evidence has shown that the success rate of manual selection remains low. The use of new incubators with integrated time-lapse imaging system is providing new possibilities for embryo assessment. As such, we address this problem by proposing an approach based on deep learning for automated embryo quality evaluation through the analysis of time-lapse images. Automatic embryo detection is complicated by the topological changes of a tracked object. Moreover, the algorithm should process a large number of image files of different qualities in a reasonable amount of time. Methods We propose an automated approach to detect human embryo development stages during incubation and to highlight embryos with abnormal behaviour by focusing on five different stages. This method encompasses two major steps. First, the location of an embryo in the image is detected by employing a Haar feature-based cascade classifier and leveraging the radiating lines. Then, a multi-class prediction model is developed to identify a total cell number in the embryo using the technique of deep learning. Results The experimental results demonstrate that the proposed method achieves an accuracy of at least 90% in the detection of embryo location. The implemented deep learning approach to identify the early stages of embryo development resulted in an overall accuracy of over 92% using the selected architectures of convolutional neural networks. The most problematic stage was the 3-cell stage, presumably due to its short duration during development. Conclusion This research contributes to the field by proposing a model to automate the monitoring of early-stage human embryo development. Unlike in other imaging fields, only a few published attempts have involved leveraging deep learning in this field. Therefore, the approach presented in this study could be used in the creation of novel algorithms integrated into the assisted reproductive technology used by embryologists.

scholarly journals Evaluation of human embryo development in in vitro fertilization- and intracytoplasmic sperm injection-fertilized oocytes: A time-lapse study

2017 ◽  
Vol 44 (2) ◽  
pp. 90 ◽  
Author(s):  
Hyung Jun Kim ◽  
Hye Jin Yoon ◽  
Jung Mi Jang ◽  
Won Don Lee ◽  
San Hyun Yoon ◽  
...  
Keyword(s):  
In Vitro Fertilization ◽  
Embryo Development ◽  
Intracytoplasmic Sperm Injection ◽  
Human Embryo ◽  
Time Lapse ◽  
Vitro Fertilization ◽  
Human Embryo Development

Time-lapse based investigation of early human embryo development: the impact of age and AMH

2012 ◽  
Vol 94 (1) ◽  
pp. 30-31
Author(s):  
J. Liebenthron ◽  
M. Montag ◽  
M. Köster ◽  
B. Toth ◽  
J. Reinsberg ◽  
...  
Keyword(s):  
Embryo Development ◽  
Human Embryo ◽  
Time Lapse ◽  
Human Embryo Development ◽  
The Impact ◽  
Early Human

Effect of oxygen concentration on human embryo development evaluated by time-lapse monitoring

2013 ◽  
Vol 99 (3) ◽  
pp. 738-744.e4 ◽  
Author(s):  
Kirstine Kirkegaard ◽  
Johnny Juhl Hindkjaer ◽  
Hans Jakob Ingerslev
Keyword(s):  
Oxygen Concentration ◽  
Embryo Development ◽  
Human Embryo ◽  
Time Lapse ◽  
Human Embryo Development ◽  
Effect Of Oxygen
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