Name
Title | ||
|---|---|---|
Technical note: automatic segmentation method of pelvic floor levator hiatus in ultrasound using a self-normalising neural network. |
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. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1117/12.2322403 | Proceedings of SPIE |
Keywords | DocType | Volume |
levator hiatus,automatic segmentation,SNN,ultrasound,CNN | Conference | 10576 |
ISSN | Citations | PageRank |
0277-786X | 0 | 0.34 |
References | Authors | |
0 | 8 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Ester Bonmati | 1 | 60 | 4.77 |
| Yipeng Hu | 2 | 170 | 14.83 |
| Nikhil Sindhwani | 3 | 3 | 1.18 |
| Hans Peter Dietz | 4 | 2 | 0.74 |
| Jan D'hooge | 5 | 284 | 32.31 |
| Dean C. Barratt | 6 | 410 | 32.01 |
| Jan Deprest | 7 | 123 | 20.45 |
| Tom Vercauteren | 8 | 1956 | 108.68 |