Efficient fetal ultrasound image segmentation for automatic head circumference measurement using a lightweight deep convolutional neural network.
Journal:
Medical physics
Published Date:
May 24, 2022
Abstract
PURPOSE: Fetal head circumference (HC) is an important biometric parameter that can be used to assess fetal development in obstetric clinical practice. Most of the existing methods use deep neural network to accomplish the task of automatic fetal HC measurement from two-dimensional ultrasound images, and some of them achieved relatively high prediction accuracy. However, few of these methods focused on optimizing model efficiency performance. Our purpose is to develop a more efficient approach for this task, which could help doctors measure HC faster and would be more suitable for deployment on devices with scarce computing resources.