Temporal Convolution Networks for Real-Time Abdominal Fetal Aorta Analysis with Ultrasound
This work addresses the need for efficient clinical diagnosis in fetal ultrasound analysis, representing an incremental improvement over existing methods.
The paper tackled the problem of automating the measurement of fetal abdominal aorta diameter from ultrasound sequences, achieving a reduction in mean squared error from 0.31 mm² to 0.09 mm² and relative error from 8.1% to 5.3% with real-time performance of 289 frames per second.
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.