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

arXiv: https://arxiv.org/abs/1712.06452 · HTML: https://mlsanity.com/paper/1712.06452/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 18 Dec 2017.

## Built on by (most cited first, 3 of 3)

- [Interactive Segmentation via Deep Learning and B-Spline Explicit Active Surfaces](https://mlsanity.com/paper/2110.12939/) (2021)
- [Voice-assisted Image Labelling for Endoscopic Ultrasound Classification using Neural Networks](https://mlsanity.com/paper/2110.06367/) (2021)
- [Recurrent U-net for automatic pelvic floor muscle segmentation on 3D ultrasound](https://mlsanity.com/paper/2107.13833/) (2021)

## Abstract (arXiv)

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, 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 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.