Abstract
The automatic analysis of ultrasound sequences can substantially improve the efficiency of clinical diagnosis. This article presents an attempt to automate the challenging task of measuring the vascular diameter of the fetal abdominal aorta from ultrasound images. We propose a neural network architecture consisting of three blocks: a convolutional neural network (CNN) for the extraction of imaging features, a convolution gated recurrent unit (C-GRU) for exploiting the temporal redundancy of the signal, and a regularized loss function, called CyclicLoss, to impose our prior knowledge about the periodicity of the observed signal. The solution is investigated with a cohort of 25 ultrasound sequences acquired during the third-trimester pregnancy check, and with 1000 synthetic sequences. In the extraction of features, it is shown that a shallow CNN outperforms two other deep CNNs with both the real and synthetic cohorts, suggesting that echocardiographic features are optimally captured by a reduced number of CNN layers. The proposed architecture, working with the shallow CNN, reaches an accuracy substantially superior to previously reported methods, providing an average reduction of the mean squared error from 0.31 (state-of-the-art) to 0.09 mathrm{mm}^2, and a relative error reduction from 8.1 to 5.3%. The mean execution speed of the proposed approach of 289 frames per second makes it suitable for real-time clinical use.
Highlights
Fetal ultrasound (US) imaging plays a fundamental role in the monitoring of fetal growth during pregnancy and in the measurement of the fetus well-being
We propose a neural network architecture consisting of three blocks: a convolutional neural network (CNN) for the extraction of imaging features, a convolution gated recurrent unit (C-GRU) for exploiting the temporal redundancy of the signal, and a regularized loss function, called CyclicLoss, to impose our prior knowledge about the periodicity of the observed signal
The deep learning (DL) architecture proposed shows excellent performance compared to traditional image analysis methods, both in accuracy and efficiency
Summary
Fetal ultrasound (US) imaging plays a fundamental role in the monitoring of fetal growth during pregnancy and in the measurement of the fetus well-being. Very few attempts toward automated assessment have been presented [14, 15], all of which have computational requirements that prevent them to be used in real time As such, they have reduced the appeal for clinical use. We describe a method for automated measurement of the abdominal aortic diameter directly from fetal US videos. We propose a neural network architecture that is able to process US videos in real time and leverage both the temporal redundancy of US videos and the quasi-periodicity of the aorta diameter. We show that a shallow CNN is able to learn imaging features better than two other deep alternatives and outperforms classical methods as level-set for fetal abdominal aorta diameter prediction. We add a new penalty term to the loss function used to train the CNN to exploit periodic variations
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