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10 January 2018 Automatic segmentation method of pelvic floor levator hiatus in ultrasound using a self-normalizing neural network
Ester Bonmati, Yipeng Hu, Nikhil Sindhwani, Hans Peter Dietz, Jan D’hooge, Dean Barratt, Jan Deprest, Tom Vercauteren
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Journal of Medical Imaging, Vol. 5, Issue 2, 021206 (January 2018). https://doi.org/10.1117/1.JMI.5.2.021206
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.
CC BY: © The Authors. Published by SPIE under a Creative Commons Attribution 4.0 Unported License. Distribution or reproduction of this work in whole or in part requires full attribution of the original publication, including its DOI.
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Ester Bonmati, Yipeng Hu, Nikhil Sindhwani, Hans Peter Dietz, Jan D’hooge, Dean Barratt, Jan Deprest, and Tom Vercauteren "Automatic segmentation method of pelvic floor levator hiatus in ultrasound using a self-normalizing neural network," Journal of Medical Imaging 5(2), 021206 (10 January 2018). https://doi.org/10.1117/1.JMI.5.2.021206
Received: 15 September 2017; Accepted: 18 December 2017; Published: 10 January 2018
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Cited by 28 scholarly publications.
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KEYWORDS
Image segmentation
Ultrasonography
Neural networks
Medical imaging
3D image processing
Convolution
Network architectures
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