Share Email Print

Proceedings Paper

Technical note: automatic segmentation method of pelvic floor levator hiatus in ultrasound using a self-normalising neural network
Author(s): Ester Bonmati; Yipeng Hu; Nikhil Sindhwani; Hans Peter Dietz; Jan D'hooge; Dean Barratt; Jan Deprest; Tom Vercauteren
Format Member Price Non-Member Price
PDF $17.00 $21.00

Paper Abstract

Segmentation of the levator hiatus in ultrasound allows to extract biometrics which are of importance for pelvic floor disorder assessment. In this work, we present a fully automatic method using a convolutional neural network (CNN) to outline the levator hiatus in a 2D image extracted from a 3D ultrasound volume. In particular, our method uses a recently developed scaled exponential linear unit (SELU) as a nonlinear self-normalising activation function. SELU has important advantages such as being parameter-free and mini-batch independent. A dataset with 91 images from 35 patients all labelled by three operators, is used for training and evaluation in a leave-one-patient-out cross-validation. Results show a median Dice similarity coefficient of 0.90 with an interquartile range of 0.08, with equivalent performance to the three operators (with a Williams’ index of 1.03), and outperforming a U-Net architecture without the need for batch normalisation. We conclude that the proposed fully automatic method achieved equivalent accuracy in segmenting the pelvic floor levator hiatus compared to a previous semi-automatic approach.

Paper Details

Date Published: 13 March 2018
PDF: 7 pages
Proc. SPIE 10576, Medical Imaging 2018: Image-Guided Procedures, Robotic Interventions, and Modeling, 105760K (13 March 2018); doi: 10.1117/12.2322403
Show Author Affiliations
Ester Bonmati, Univ. College London (United Kingdom)
Yipeng Hu, Univ. College London (United Kingdom)
Nikhil Sindhwani, KU Leuven (Belgium)
Hans Peter Dietz, Sydney Medical School Nepean (Australia)
Jan D'hooge, KU Leuven (Belgium)
Dean Barratt, Univ. College London (United Kingdom)
Jan Deprest, Univ. College London (United Kingdom)
KU Leuven (Belgium)
Tom Vercauteren, Univ. College London (United Kingdom)
KU Leuven (Belgium)

Published in SPIE Proceedings Vol. 10576:
Medical Imaging 2018: Image-Guided Procedures, Robotic Interventions, and Modeling
Baowei Fei; Robert J. Webster III, Editor(s)

© SPIE. Terms of Use
Back to Top
Sign in to read the full article
Create a free SPIE account to get access to
premium articles and original research
Forgot your username?