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Feasibility of a deep learning-based method for automated localization of pelvic floor landmarks using stress MR images

INTRODUCTION AND HYPOTHESIS: Magnetic resonance (MR) imaging plays an important role in assessing pelvic organ prolapse (POP), and automated pelvic floor landmark localization potentially accelerates MR-based measurements of POP. We herein aimed to develop and evaluate a deep learning-based techniqu...

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Bibliographic Details
Published in:Int Urogynecol J
Main Authors: Feng, Fei, Ashton-Miller, James A., DeLancey, John O.L., Luo, Jiajia
Format: Artigo
Language:Inglês
Published: 2021
Subjects:
Online Access:https://ncbi.nlm.nih.gov/pmc/articles/PMC8292443/
https://ncbi.nlm.nih.gov/pubmed/33475815
https://ncbi.nlm.nih.govhttp://dx.doi.org/10.1007/s00192-020-04626-5
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