Name
Title | ||
|---|---|---|
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 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 inter-quartile 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. (c) The Authors. Published by SPIE under a Creative Commons Attribution 3.0 Unported License. Distribution or reproduction of this work in whole or in part requires full attribution of the original publication, including its DOI. |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1117/1.JMI.5.2.021206 | JOURNAL OF MEDICAL IMAGING |
Keywords | Field | DocType |
levator hiatus,automatic segmentation,self-normalizing neural network,ultrasound,convolutional neural network | Normalization (statistics),Pattern recognition,Convolutional neural network,Medical imaging,Segmentation,Computer science,Pelvic floor,Artificial intelligence,Biometrics,Artificial neural network,Cross-validation | Journal |
Volume | Issue | ISSN |
5 | 2 | 2329-4302 |
Citations | PageRank | References |
2 | 0.41 | 12 |
Authors | ||
8 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Ester Bonmati | 1 | 4 | 1.15 |
| 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 | 2 | 0.74 |
| Jan Deprest | 7 | 123 | 20.45 |
| Tom Vercauteren | 8 | 1956 | 108.68 |