Fetal Ultrasound Image Segmentation for Measuring Biometric Parameters Using Multi-Task Deep Learning

TL;DR

This paper introduces a multi-task deep learning approach for automatic fetal head segmentation and biometric measurement from ultrasound images, improving accuracy and efficiency in prenatal diagnosis.

Contribution

A novel multi-task deep convolutional neural network is developed for simultaneous segmentation and biometric estimation, outperforming existing methods.

Findings

Segmentation dice scores are comparable to state-of-the-art.

HC measurement accuracy matches radiologist annotations.

Method performs well across different pregnancy trimesters.

Abstract

Ultrasound imaging is a standard examination during pregnancy that can be used for measuring specific biometric parameters towards prenatal diagnosis and estimating gestational age. Fetal head circumference (HC) is one of the significant factors to determine the fetus growth and health. In this paper, a multi-task deep convolutional neural network is proposed for automatic segmentation and estimation of HC ellipse by minimizing a compound cost function composed of segmentation dice score and MSE of ellipse parameters. Experimental results on fetus ultrasound dataset in different trimesters of pregnancy show that the segmentation results and the extracted HC match well with the radiologist annotations. The obtained dice scores of the fetal head segmentation and the accuracy of HC evaluations are comparable to the state-of-the-art.