  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.
