An accurate estimation of gestational age is critical for prenatal care and clinical decision-making. Existing ultrasound-based gestational age estimation methods are limited by the insufficient information representation capacity of conventional medical segmentation models, noise interference in ultrasound images, and inter-observer variability in traditional geometry-based measurement methods. To address these challenges, we propose the MsGA model to estimate gestational age with multi-plane unified measurements driven by anatomic segmentation. In the anatomic segmentation stage, a lightweight and high-performance LGF-UNet module is proposed, which utilizes the Deep Patch Embedding module to expand the receptive field, the Local-Global Fusion Transformer block to enhance local-global feature fusion, and the Focusing Attention Bottleneck module to suppress ultrasound noise via an adaptive threshold. In the measurement stage, a Point Regression module is introduced to refine biometric landmark localization. Furthermore, we create a fully annotated ultrasound plane dataset for the estimation of gestational age across various gestational stages. Extensive experiments on the dataset have demonstrated the effectiveness of the whole model and each module. Our MsGA model is superior to existing models with fewer parameters and achieves state-of-the-art performance on the Gestational Age Estimation task.
Huang et al. (Thu,) studied this question.