Title: Temporal Convolution Networks for Real-Time Abdominal Fetal Aorta Analysis with Ultrasound,
Abstract: The automatic analysis of ultrasound sequences can substantially improve the
efficiency of clinical diagnosis. In this work we present our 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 layer for the
extraction of imaging features, a Convolution Gated Recurrent Unit (C-GRU) for
enforcing the temporal coherence across video frames and exploiting the
temporal redundancy of a signal, and a regularized loss function, called
\textit{CyclicLoss}, to impose our prior knowledge about the periodicity of the
observed signal. We present experimental evidence suggesting that the proposed
architecture can reach an accuracy substantially superior to previously
proposed methods, providing an average reduction of the mean squared error from
$0.31 mm^2$ (state-of-art) to $0.09 mm^2$, 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.