Enhanced Fetal Ultrasound Image Segmentation using Spatial Attention Mechanisms with UNet: SAUnet

Fetal Ultrasound (US) imaging is necessary to prenatal treatment, as it provides information on the health and development of the fetus. Accurate segmenting is essential but poses significant challenges due to the dynamic fetal anatomy. This paper proposes Unet with Spatial Attention (SAUnet), a novel deep learning architecture that integrates attention mechanisms into the Unet framework. Unlike traditional models, SAUnet dynamically emphasizes key anatomical structures while suppressing irrelevant background information, enhancing the model’s focus on informative regions within the US images. This targeted approach leads to improved segmentation accuracy and robustness. Our method was evaluated on the HC18 Challenge dataset, achieving a mean absolute difference(±std) of 2.23 ± 2.41 mm in head circumference measure and a mean Dice similarity coefficient of 97.63 ± 1.85%, demonstrating high segmentation accuracy. Additionally, mean difference and Hausdorff distance are 1.06 ± 3.11 mm and 1.42 ± 0.93 mm respectively suggesting robust performance. Incorporation of spatial attention in Unet architecture marks a significant advancement in fetal US image segmentation, offering a lightweight and efficient model suitable for real-time clinical deployment. Benchmark comparisons highlight SAUnet’s competitive performance, reinforcing its potential as a state-of-the-art solution for prenatal imaging tasks.