Fetal Ultrasound Image Segmentation for Measuring Biometric Parameters Using Multi-Task Deep Learning
This work addresses the problem of automating biometric measurements for prenatal diagnosis, which is incremental as it builds on existing segmentation and estimation techniques.
The paper tackled automatic segmentation and head circumference (HC) estimation from fetal ultrasound images using a multi-task deep learning approach, achieving dice scores and HC accuracy comparable to state-of-the-art methods.
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.