Localization of Fetal Head in Ultrasound Images by Multiscale View and Deep Neural Networks

TL;DR

This paper introduces a lightweight deep neural network utilizing multiscale inputs for automatic fetal head circumference measurement in ultrasound images, achieving comparable accuracy to deeper models with fewer parameters.

Contribution

A novel light convolutional neural network leveraging multiscale inputs for efficient fetal head measurement in ultrasound images.

Findings

Comparable segmentation accuracy to deep CNNs

Fewer parameters and reduced training time

Effective across different pregnancy trimesters

Abstract

One of the routine examinations that are used for prenatal care in many countries is ultrasound imaging. This procedure provides various information about fetus health and development, the progress of the pregnancy and, the baby's due date. Some of the biometric parameters of the fetus, like fetal head circumference (HC), must be measured to check the fetus's health and growth. In this paper, we investigated the effects of using multi-scale inputs in the network. We also propose a light convolutional neural network for automatic HC measurement. Experimental results on an ultrasound dataset of the fetus in different trimesters of pregnancy show that the segmentation accuracy and HC evaluations performed by a light convolutional neural network are comparable to deep convolutional neural networks. The proposed network has fewer parameters and requires less training time.