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Journal of Medical Imaging • Open Access

Automatic segmentation method of pelvic floor levator hiatus in ultrasound using a self-normalizing neural network
Author(s): Ester Bonmati; Yipeng Hu; Nikhil Sindhwani; Hans Peter Dietz; Jan D’hooge; Dean Barratt; Jan Deprest; Tom Vercauteren

Paper Abstract

Segmentation of the levator hiatus in ultrasound allows the extraction of biometrics, which are of importance for pelvic floor disorder assessment. We present a fully automatic method using a convolutional neural network (CNN) to outline the levator hiatus in a two-dimensional image extracted from a three-dimensional ultrasound volume. In particular, our method uses a recently developed scaled exponential linear unit (SELU) as a nonlinear self-normalizing activation function, which for the first time has been applied in medical imaging with CNN. SELU has important advantages such as being parameter-free and mini-batch independent, which may help to overcome memory constraints during training. A dataset with 91 images from 35 patients during Valsalva, contraction, and rest, all labeled 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 normalization. We conclude that the proposed fully automatic method achieved equivalent accuracy in segmenting the pelvic floor levator hiatus compared to a previous semiautomatic approach.

Paper Details

Date Published: 10 January 2018
PDF: 8 pages
J. Med. Imag. 5(2) 021206 doi: 10.1117/1.JMI.5.2.021206
Published in: Journal of Medical Imaging Volume 5, Issue 2
Show Author Affiliations
Ester Bonmati, Univ. College London (United Kingdom)
Yipeng Hu, Univ. College London (United Kingdom)
Nikhil Sindhwani, UZ Leuven (Belgium)
Hans Peter Dietz, The Univ. of Sydney (Australia)
New South Wales Government (Australia)
Jan D’hooge, UZ Leuven (Belgium)
Dean Barratt, Univ. College London (United Kingdom)
Jan Deprest, Univ. College London (United Kingdom)
Tom Vercauteren, Univ. College London (United Kingdom)


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