Bonmati, E; Hu, Y; Sindhwani, N; Dietz, HP; D'hooge, J; Barratt, DC; Deprest, J; Bonmati, E; Hu, Y; Sindhwani, N; Dietz, HP; D'hooge, J; Barratt, DC; Deprest, J; Vercauteren, T; - view fewer (2018) Technical note: automatic segmentation method of pelvic floor levator hiatus in ultrasound using a self-normalising neural network. In: Fei, B and Webster, RJ, (eds.) (Proceedings) Medical Imaging 2018: Image-Guided Procedures, Robotic Interventions and Modeling. (pp. 105760K-105760K). SPIE

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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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