Automatic segmentation method of pelvic floor levator hiatus in ultrasound using a self-normalizing neural network.

Ester Bonmati,Dean Barratt,Hans Peter Dietz,Jan D'Hooge,Jan Deprest,Nikhil Sindhwani,Tom Vercauteren,Yipeng Hu

doi:10.1117/1.jmi.5.2.021206

Ester Bonmati, Dean Barratt + Show 6 more

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https://doi.org/10.1117/1.jmi.5.2.021206

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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.

Highlights

Pelvic organ prolapse (POP) is the abnormal downward descent of pelvic organs, including the bladder, uterus, and/or the rectum or small bowel, through the genital hiatus, resulting in a protrusion through the vagina
We propose a fully automatic method to segment, in manually defined 2-D C-planes, the levator hiatus from ultrasound volumes thereby further automating the process of outlining the pelvic floor
We report the results obtained with the baseline U-Net architecture, and the U-Net + DC and ResNet architectures

Summary

Introduction

Pelvic organ prolapse (POP) is the abnormal downward descent of pelvic organs, including the bladder, uterus, and/or the rectum or small bowel, through the genital hiatus, resulting in a protrusion through the vagina. 27,342 women between the age of 50 and 79 years were examined and found that about 41% showed some degree of prolapsed.[1] Ultrasound is at present the most widely used imaging modality to assess the anatomical integrity and function of pelvic floor because of availability and noninvasiveness. Since the levator hiatus is the portal through which POP must occur, its dimensions and appearance are measured and recorded during an ultrasound exam. The hiatal dimensions have been correlated with severity of prolapse, levator muscle avulsion, and even prolapse recurrence after surgery.[2,3,4]. During a transperineal ultrasound examination, three-dimensional (3-D) volumes are acquired during Valsalva maneuver (act of expiration while closing the airways after a full inspiration), at pelvic floor muscle contraction, and during rest. The hiatal dimensions and its area are recorded by manually outlining the levator hiatus in the oblique axial two-dimensional (2-D) plane at the level of minimal anterioposterior hiatal dimensions (referred to as the C-plane hereinafter).[2]

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