|Title||Automatic segmentation method of pelvic floor levator hiatus in ultrasound using a self-normalizing neural network|
|Authors||Bonmati Coll, E., Hu, Y., Sindhwani, N., Dietz, H.P., D'hooge, J., Barratt, D., Deprest, J. and Vercauteren, T.|
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.
|Journal||Journal of Medical Imaging|
|Journal citation||5 (2)|
|Digital Object Identifier (DOI)||https://doi.org/10.1117/1.jmi.5.2.021206|
|Published||10 Jan 2018|