Bonmati Coll, E., Hu, Y., Sindhwani, N., Dietz, H.P., D'hooge, J., Barratt, D., Deprest, J. and Vercauteren, T. 2018. Automatic segmentation method of pelvic floor levator hiatus in ultrasound using a self-normalizing neural network. Journal of Medical Imaging. 5 (2) 021206. https://doi.org/10.1117/1.jmi.5.2.021206
| Title | Automatic segmentation method of pelvic floor levator hiatus in ultrasound using a self-normalizing neural network |
|---|---|
| Type | Journal article |
| Authors | Bonmati Coll, E., Hu, Y., Sindhwani, N., Dietz, H.P., D'hooge, J., Barratt, D., Deprest, J. and Vercauteren, T. |
| Abstract | 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. |
| Article number | 021206 |
| Journal | Journal of Medical Imaging |
| Journal citation | 5 (2) |
| ISSN | 2329-4310 |
| 2329-4302 | |
| Year | 2018 |
| Publisher | SPIE |
| Digital Object Identifier (DOI) | https://doi.org/10.1117/1.jmi.5.2.021206 |
| Publication dates | |
| Published | 10 Jan 2018 |