APPLAUSE: Automatic Prediction of PLAcental health via U-net Segmentation and statistical Evaluation
Maximilian Pietsch,Alison Ho,Alessia Bardanzellu,Aya Zeidan,Lucy C Chappell,Joseph V. Hajnal,Mary A. Rutherford,Jana Hutter +7 more
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TLDR
In this paper, the authors proposed a fully automatic pipeline to predict the biological age and health of the placenta based on a free-breathing rapid (sub-30 second) T2* scan in two steps: automatic segmentation using a U-Net and a Gaussian process regression model to characterize placental maturation and health.About:
This article is published in Medical Image Analysis.The article was published on 2021-06-23 and is currently open access. It has received 13 citations till now. The article focuses on the topics: Placental insufficiency & Population.read more
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Journal ArticleDOI
Artificial intelligence applied to fetal MRI: A scoping review of current research
TL;DR: A literature search of the current state-of-the-art and emerging trends for the use of artificial intelligence as applied to fetal magnetic resonance imaging (MRI) yielded several publications covering AI tools for anatomical organ segmentation, improved imaging sequences and aiding in diagnostic applications such as automated biometric fetal measurements.
Book ChapterDOI
A Bootstrap Self-training Method for Sequence Transfer: State-of-the-Art Placenta Segmentation in fetal MRI
Bella Specktor-Fadida,Daphna Link-Sourani,Shai Ferster-Kveller,Liat Ben-Sira,Elka Miller,Dafna Ben-Bashat,Leo Joskowicz +6 more
TL;DR: In this article, a new method for bootstrapping automatic placenta segmentation by deep learning on different MRI sequences is presented, which consists of automatic segmentation with two networks trained on labeled cases of one sequence followed by automatic adaptation using self-training of the same network to a new sequence with new unlabeled cases of this sequence.
Journal ArticleDOI
SCU-Net++: A Nested U-Net Based on Sharpening Filter and Channel Attention Mechanism
Hu Cui,Haiwei Pan,Kejia Zhang +2 more
TL;DR: An improved semantic segmentation model utilizing channel attention mechanism and Laplacian sharpening filter is proposed for SCU-Net++: dense skip connections are redesigned with sharpening filters to ease the semantic gaps, and channel attention modules are used to make the model pay more attention on the feature maps that are useful for the pixel-level classification task.
Journal ArticleDOI
The Role of Inorganics in Preeclampsia Assessed by Multiscale Multimodal Characterization of Placentae
Thomas Rduch,Elena Tsolaki,Yassir El Baz,Sebastian Leschka,Diana Born,Janis Kinkel,Alexandre H. C. Anthis,Tina Fischer,Wolfram Jochum,Rene Hornung,Alexander Gogos,Inge K. Herrmann +11 more
TL;DR: It is suggested that heavy metals, combined with other factors, can be associated with the development of preeClampsia, however, with no obvious correlation between calcifications and preeclampsia.
Journal ArticleDOI
Micro-haemodynamics at the maternal–fetal interface: experimental, theoretical and clinical perspectives
Qi Zhou,Eleanor Doman,Kerstin Schirrmann,Qi Chen,Ellie Seed,Edward D. Johnstone,Ponnambalam Ravi Selvaganapathy,Anne Juel,Oliver E. Jensen,Miguel O. Bernabeu,Timm Krüger,Igor L. Chernyavsky +11 more
TL;DR: The placenta is a vital interface between the mother and her developing fetus, where the particulate nature of blood flow cannot be ignored, mediating the relationship between the organ's structure and its function as mentioned in this paper .
References
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Proceedings Article
Adam: A Method for Stochastic Optimization
Diederik P. Kingma,Jimmy Ba +1 more
TL;DR: This work introduces Adam, an algorithm for first-order gradient-based optimization of stochastic objective functions, based on adaptive estimates of lower-order moments, and provides a regret bound on the convergence rate that is comparable to the best known results under the online convex optimization framework.
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Gaussian processes in machine learning
TL;DR: In this paper, the authors give a basic introduction to Gaussian Process regression models and present the simple equations for incorporating training data and examine how to learn the hyperparameters using the marginal likelihood.