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Spontaneous preterm birth prediction using convolutional neural networks
An estimated 15 million babies are born too early every year. Approximat...
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Automatic Estimation of Fetal Abdominal Circumference from Ultrasound Images
Ultrasound diagnosis is routinely used in obstetrics and gynecology for ...
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A Deep Learning-based Method to Extract Lumen and Media-Adventitia in Intravascular Ultrasound Images
Intravascular ultrasound (IVUS) imaging allows direct visualization of t...
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Automated segmentation of an intensity calibration phantom in clinical CT images using a convolutional neural network
Purpose: To apply a convolutional neural network (CNN) to develop a syst...
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Fully Automatic Segmentation of 3D Brain Ultrasound: Learning from Coarse Annotations
Intra-operative ultrasound is an increasingly important imaging modality...
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Simultaneous Segmentation and Classification of Bone Surfaces from Ultrasound Using a Multi-feature Guided CNN
Various imaging artifacts, low signal-to-noise ratio, and bone surfaces ...
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Reliable Liver Fibrosis Assessment from Ultrasound using Global Hetero-Image Fusion and View-Specific Parameterization
Ultrasound (US) is a critical modality for diagnosing liver fibrosis. Un...
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Automatic segmentation method of pelvic floor levator hiatus in ultrasound using a self-normalising neural network
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, 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 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.
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