Deep learning enables automatic quantitative assessment of puborectalis muscle and urogenital hiatus in plane of minimal hiatal dimensions.

Frieda van den Noort Huub van der Vaart A T M Grob Maria K van de Waarsenburg Cornelis H Slump Marijn van Stralen

Published in: Ultrasound in obstetrics & gynecology : the official journal of the International Society of Ultrasound in Obstetrics and Gynecology (2019)

Deep learning can be used to segment automatically and reliably the PRM and UH on 2D ultrasound images of the nulliparous pelvic floor in the plane of minimal hiatal dimensions. These segmentations can be used to measure reliably UH dimensions as well as PRM length and MEP. © 2018 The Authors. Ultrasound in Obstetrics & Gynecology published by John Wiley & Sons Ltd on behalf of the International Society of Ultrasound in Obstetrics and Gynecology.

Keyphrases

deep learning
magnetic resonance imaging
convolutional neural network
artificial intelligence
machine learning
contrast enhanced ultrasound
ultrasound guided
skeletal muscle
randomized controlled trial
gastroesophageal reflux disease
computed tomography
optical coherence tomography