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J Med Imaging (Bellingham). 2018 Apr;5(2):021206. doi: 10.1117/1.JMI.5.2.021206. Epub 2018 Jan 10.

Automatic segmentation method of pelvic floor levator hiatus in ultrasound using a self-normalizing neural network.

Author information

1
University College London, Centre for Medical Image Computing, London, United Kingdom.
2
University College London, Wellcome/EPSRC Centre for Interventional and Surgical Sciences, London, United Kingdom.
3
University College London, Department of Medical Physics and Biomedical Engineering, London, United Kingdom.
4
University Hospitals Leuven, Department of Development and Regeneration, Cluster Urogenital Surgery and Clinical Department of Obstetrics and Gynaecology, KU Leuven, Leuven, Belgium.
5
Sydney Medical School Nepean, Nepean Hospital, Penrith, Australia.

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.

KEYWORDS:

automatic segmentation; convolutional neural network; levator hiatus; self-normalizing neural network; ultrasound

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