MVSegNet: A Lightweight Boundary-Aware Network for Fetal Lateral Ventricle Segmentation and Atrial Width Estimation in Prenatal Ultrasound
Researchers have developed MVSegNet, a new lightweight neural network designed for segmenting fetal lateral ventricles and estimating atrial width in prenatal ultrasounds. This model addresses challenges like noise and poor contrast in ultrasound images. MVSegNet demonstrated superior performance in boundary detection and measurement accuracy compared to six other segmentation methods, while maintaining computational efficiency. AI
IMPACT Enhances diagnostic accuracy in prenatal ultrasounds, potentially leading to earlier detection of fetal abnormalities.