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

Purpose Automated segmentation could improve the efficiency of modeling‐based pelvic organ prolapse (POP) evaluations. However, segmentation performance is limited by the blurry soft tissue boundaries. In this study, we aimed to present a hybrid solution for uterus, rectum, bladder, and levator ani...

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Veröffentlicht in:	Medical physics (Lancaster) Jg. 47; H. 9; S. 4281 - 4293
Hauptverfasser:	Feng, Fei, Ashton‐Miller, James A., DeLancey, John O. L., Luo, Jiajia
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	United States 01.09.2020
Schlagworte:	convolutional neural network Female Humans Image Processing, Computer-Assisted level set Magnetic Resonance Imaging MRI segmentation multiresolution features pyramid Neural Networks, Computer Pelvic Floor - diagnostic imaging pelvic organ prolapse Pelvic Organ Prolapse - diagnostic imaging multiresolution features pyramid MRI segmentation pelvic organ prolapse convolutional neural network level set
ISSN:	0094-2405, 2473-4209, 2473-4209
Online-Zugang:	Volltext
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