Temporal Convolution Networks for Real-Time Abdominal Fetal Aorta Analysis with Ultrasound

Conference paper


Savioli, N., Visentin, S., Cosmi, E., Grisan, E., Lamata, P. and Montana, G. (2018). Temporal Convolution Networks for Real-Time Abdominal Fetal Aorta Analysis with Ultrasound. Artificial Neural Networks and Machine Learning – ICANN 2018. Rhodes, Greece 04 - 07 Oct 2018 Springer. doi:10.1007/978-3-030-01421-6_15
AuthorsSavioli, N., Visentin, S., Cosmi, E., Grisan, E., Lamata, P. and Montana, G.
TypeConference paper
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 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 0.31mm2 (state-of-art) to 0.09mm2, 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. © Springer Nature Switzerland AG 2018.

Year2018
PublisherSpringer
ISSN0302-9743
Digital Object Identifier (DOI)doi:10.1007/978-3-030-01421-6_15
Accepted author manuscript
License
CC BY
File Access Level
Open
Publication dates
Print26 Sep 2018
Publication process dates
Accepted01 Jun 2018
Deposited27 Nov 2019
Book titleLecture Notes in Computer Science
ISBN978-3-030-01420-9
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https://openresearch.lsbu.ac.uk/item/88961

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