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

Bonmati Coll, E., Hu, Y., Sindhwani, N., Dietz, H.P., D'hooge, J., Barratt, D., Deprest, J. and Vercauteren, T. 2018. Automatic segmentation method of pelvic floor levator hiatus in ultrasound using a self-normalizing neural network. Journal of Medical Imaging. 5 (2) 021206. https://doi.org/10.1117/1.jmi.5.2.021206

TitleAutomatic segmentation method of pelvic floor levator hiatus in ultrasound using a self-normalizing neural network
TypeJournal article
AuthorsBonmati Coll, E., Hu, Y., Sindhwani, N., Dietz, H.P., D'hooge, J., Barratt, D., Deprest, J. and Vercauteren, T.
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.

Article number021206
JournalJournal of Medical Imaging
Journal citation5 (2)
ISSN2329-4310
2329-4302
Year2018
PublisherSPIE
Digital Object Identifier (DOI)https://doi.org/10.1117/1.jmi.5.2.021206
Publication dates
Published10 Jan 2018

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