Convolutional neural network‐based pelvic floor structure segmentation using magnetic resonance imaging in pelvic organ prolapse-Reference-Cited by-同舟云学术

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Convolutional neural network‐based pelvic floor structure segmentation using magnetic resonance imaging in pelvic organ prolapse

Published:2020-07-28 Issue:9 Volume:47 Page:4281-4293
ISSN:0094-2405
Container-title:Medical Physics
language:en
Short-container-title:Med. Phys.

Author:

Feng Fei¹,Ashton‐Miller James A.²,DeLancey John O. L.³,Luo Jiajia⁴

Affiliation:

1. University of Michigan‐Shanghai Jiao Tong University Joint Institute Shanghai Jiao Tong University Shanghai200240China

2. Department of Mechanical Engineering University of Michigan Ann Arbor MI48109USA

3. Department of Obstetrics and Gynecology University of Michigan Ann Arbor MI48109USA

4. Biomedical Engineering Department Peking University Beijing100191China

Publisher

Wiley

Subject

General Medicine

Link

https://onlinelibrary.wiley.com/doi/pdf/10.1002/mp.14377

Reference44 articles.

1. Procedures for pelvic organ prolapse in the United States, 1979-1997

2. Cost of pelvic organ prolapse surgery in the United States

3. A 3D finite element model of anterior vaginal wall support to evaluate mechanisms underlying cystocele formation

4. A multi-compartment 3-D finite element model of rectocele and its interaction with cystocele

5. Segmentations of MRI images of the female pelvic floor: A study of inter- and intra-reader reliability

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