Deep learning–based segmentation of whole-body fetal MRI and fetal weight estimation: assessing performance, repeatability, and reproducibility-Reference-Cited by-同舟云学术

Deep learning–based segmentation of whole-body fetal MRI and fetal weight estimation: assessing performance, repeatability, and reproducibility

Published:2023-09-02 Issue: Volume: Page:
ISSN:1432-1084
Container-title:European Radiology
language:en
Short-container-title:Eur Radiol

Author:

Specktor-Fadida Bella^ORCID,Link-Sourani Daphna,Rabinowich Aviad,Miller Elka^ORCID,Levchakov Anna,Avisdris Netanell^ORCID,Ben-Sira Liat,Hiersch Liran,Joskowicz Leo,Ben-Bashat Dafna

Funder

Israel Innovation Authority

Publisher

Springer Science and Business Media LLC

Subject

Radiology, Nuclear Medicine and imaging,General Medicine

Link

https://link.springer.com/content/pdf/10.1007/s00330-023-10038-y.pdf

Reference34 articles.

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2. Rizzo G, Mappa I, Bitsadze V et al (2020) Role of Doppler ultrasound at time of diagnosis of late-onset fetal growth restriction in predicting adverse perinatal outcome: prospective cohort study. Ultrasound Obstet Gynecol 55(6):793–798. https://doi.org/10.1002/uog.20406

3. Peleg D, Kennedy CM, Hunter SK (1998) Intrauterine growth restriction: identification and management. Am Fam Physician 58(2):453

4. Milner J, Jane A (2018) The accuracy of ultrasound estimation of fetal weight in comparison to birth weight: a systematic review. Ultrasound 26(1):32–41. https://doi.org/10.1177/1742271X17732807

5. Kacem Y, Cannie MM, Kadji C et al (2013) Fetal weight estimation: comparison of two-dimensional US and MR imaging assessments. Radiology 267(3):902–910. https://doi.org/10.1148/radiol.12121374

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