Bonmati, E;
Hu, Y;
Sindhwani, N;
Dietz, HP;
D'hooge, J;
Barratt, DC;
Deprest, J;
(2018)
Technical note: automatic segmentation method of pelvic floor levator hiatus in ultrasound using a self-normalising neural network.
In: Fei, B and Webster, RJ, (eds.)
(Proceedings) Medical Imaging 2018: Image-Guided Procedures, Robotic Interventions and Modeling.
(pp. 105760K-105760K).
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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.
| Type: | Proceedings paper |
|---|---|
| Title: | Technical note: automatic segmentation method of pelvic floor levator hiatus in ultrasound using a self-normalising neural network |
| Event: | Medical Imaging 2018: Image-Guided Procedures, Robotic Interventions and Modeling |
| ISBN-13: | 9781510616417 |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1117/12.2322403 |
| Publisher version: | https://doi.org/10.1117/12.2322403 |
| Language: | English |
| Additional information: | This version is the version of record. For information on re-use, please refer to the publisher’s terms and conditions. |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Med Phys and Biomedical Eng |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10048662 |
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