Atlas-Based Segmentation of the Human Embryo Using Deep Learning with Minimal Supervision

2020 ◽  
pp. 211-221
Author(s):  
Wietske A. P. Bastiaansen ◽  
Melek Rousian ◽  
Régine P. M. Steegers-Theunissen ◽  
Wiro J. Niessen ◽  
Anton Koning ◽  
...  
Keyword(s):  
Deep Learning ◽  
Human Embryo

scholarly journals Improved monitoring of human embryo culture conditions using a deep learning-derived key performance indicator (KPI)

2019 ◽  
Vol 112 (3) ◽  
pp. e70-e71 ◽  
Author(s):  
Manoj Kumar Kanakasabapathy ◽  
Prudhvi Thirumalaraju ◽  
Raghav Gupta ◽  
Rohan Pooniwala ◽  
Hemanth Kandula ◽  
...  
Keyword(s):  
Deep Learning ◽  
Embryo Culture ◽  
Human Embryo ◽  
Performance Indicator ◽  
Culture Conditions ◽  
Key Performance Indicator ◽  
Human Embryo Culture

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 Automated Evaluation of Human Embryo Blastulation and Implantation Potential using Deep‐Learning

2020 ◽  
Vol 2 (10) ◽  
pp. 2000080 ◽  
Author(s):  
Yoav Kan-Tor ◽  
Nir Zabari ◽  
Ity Erlich ◽  
Adi Szeskin ◽  
Tamar Amitai ◽  
...  
Keyword(s):  
Deep Learning ◽  
Human Embryo ◽  
Automated Evaluation

Deep Learning for Human Embryo Classification at the Cleavage Stage (Day 3)

2021 ◽  
pp. 278-292
Author(s):  
Astrid Zeman ◽  
Anne-Sofie Maerten ◽  
Annemie Mengels ◽  
Lie Fong Sharon ◽  
Carl Spiessens ◽  
...  
Keyword(s):  
Deep Learning ◽  
Human Embryo ◽  
Cleavage Stage

Deep Learning

2009 ◽  
Author(s):  
Stellan Ohlsson
Keyword(s):  
Deep Learning

Intelligence artificielle : le futur de l’Orthodontie ?

2019 ◽  
Vol 53 (3) ◽  
pp. 281-294
Author(s):  
Jean-Michel Foucart ◽  
Augustin Chavanne ◽  
Jérôme Bourriau
Keyword(s):  
Big Data ◽  
Deep Learning ◽  
Intelligence Artificielle ◽  
Set Up

Nombreux sont les apports envisagés de l’Intelligence Artificielle (IA) en médecine. En orthodontie, plusieurs solutions automatisées sont disponibles depuis quelques années en imagerie par rayons X (analyse céphalométrique automatisée, analyse automatisée des voies aériennes) ou depuis quelques mois (analyse automatique des modèles numériques, set-up automatisé; CS Model +, Carestream Dental™). L’objectif de cette étude, en deux parties, est d’évaluer la fiabilité de l’analyse automatisée des modèles tant au niveau de leur numérisation que de leur segmentation. La comparaison des résultats d’analyse des modèles obtenus automatiquement et par l’intermédiaire de plusieurs orthodontistes démontre la fiabilité de l’analyse automatique; l’erreur de mesure oscillant, in fine, entre 0,08 et 1,04 mm, ce qui est non significatif et comparable avec les erreurs de mesures inter-observateurs rapportées dans la littérature. Ces résultats ouvrent ainsi de nouvelles perspectives quand à l’apport de l’IA en Orthodontie qui, basée sur le deep learning et le big data, devrait permettre, à moyen terme, d’évoluer vers une orthodontie plus préventive et plus prédictive.

Vollautomatische Quantifizierung der Volumina und Funktion des linken Ventrikels in der kardialen MRT: Evaluation eines Deep-Learning basierten Algorithmus

2020 ◽  
Author(s):  
B Böttcher ◽  
E Beller ◽  
A Busse ◽  
F Streckenbach ◽  
M Weber ◽  
...  
Keyword(s):  
Deep Learning

Primäre ZNS-Lymphome: Vollautomatisierte Detektion und Segmentierung auf multiparametrischen MRT-Datensätzen mittels Deep-Learning

2020 ◽  
Author(s):  
L Pennig ◽  
L Lourenco Caldeira ◽  
C Hoyer ◽  
L Görtz ◽  
R Shahzad ◽  
...  
Keyword(s):  
Deep Learning
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