Technical note: automatic segmentation method of pelvic floor levator hiatus in ultrasound using a self-normalising neural network

Ester Bonmati; Yipeng Hu; Nikhil Sindhwani; Hans Peter Dietz; Jan D'hooge; Dean Barratt; Jan Deprest; Tom Vercauteren

doi:10.1117/12.2322403

13 March 2018 Technical note: automatic segmentation method of pelvic floor levator hiatus in ultrasound using a self-normalising neural network

Ester Bonmati, Yipeng Hu, Nikhil Sindhwani, Hans Peter Dietz, Jan D'hooge, Dean Barratt, Jan Deprest, Tom Vercauteren

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Proceedings Volume 10576, Medical Imaging 2018: Image-Guided Procedures, Robotic Interventions, and Modeling; 105760K (2018) https://doi.org/10.1117/12.2322403
Event: SPIE Medical Imaging, 2018, Houston, Texas, United States

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.

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Ester Bonmati, Yipeng Hu, Nikhil Sindhwani, Hans Peter Dietz, Jan D'hooge, Dean Barratt, Jan Deprest, and Tom Vercauteren "Technical note: automatic segmentation method of pelvic floor levator hiatus in ultrasound using a self-normalising neural network", Proc. SPIE 10576, Medical Imaging 2018: Image-Guided Procedures, Robotic Interventions, and Modeling, 105760K (13 March 2018); https://doi.org/10.1117/12.2322403

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KEYWORDS

Image segmentation

Ultrasonography

Network architectures

Neural networks

Medical imaging

Distance measurement

Convolutional neural networks

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