A Bootstrap Self-training Method for Sequence Transfer: State-of-the-Art Placenta Segmentation in fetal MRI-Reference-Cited by-同舟云学术

A Bootstrap Self-training Method for Sequence Transfer: State-of-the-Art Placenta Segmentation in fetal MRI

Published:2021 Issue: Volume: Page:189-199
ISSN:0302-9743
Container-title:Uncertainty for Safe Utilization of Machine Learning in Medical Imaging, and Perinatal Imaging, Placental and Preterm Image Analysis
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Author:

Specktor-Fadida Bella,Link-Sourani Daphna,Ferster-Kveller Shai,Ben-Sira Liat,Miller Elka,Ben-Bashat Dafna,Joskowicz Leo

Publisher

Springer International Publishing

Link

https://link.springer.com/content/pdf/10.1007/978-3-030-87735-4_18

Reference24 articles.

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2. Dahdouh, S., et al.: In vivo placental MRI shape and textural features predict fetal growth restriction and postnatal outcome. J. Magn. Resonan. Imaging 47(2), 449–458 (2018)

3. Kveller-Fenster, S.: Placenta segmentation in fetal MRI scans by deep learning: a bootstrapping approach. MSc. thesis, The Hebrew University of Jerusalem, March 2021

4. Lecture Notes in Computer Science;A Alansary,2016

5. Torrents-Barrena, J., et al.: Fully automatic 3D reconstruction of the placenta and its peripheral vasculature in intrauterine fetal MRI. Med. Image Anal. 54, 263–279 (2019)

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