FetalSegNet: Automatic Segmentation of the Fetus and Sub-Anatomical Structures in Prenatal Ultrasound Images Using Hybrid Deep Learning Models

Background/Objectives: Automatic segmentation of fetal anatomical structures in prenatal ultrasound images is challenging because of low tissue contrast, noise caused by speckles, anatomical variability and the fact that structures are only partially visible within individual image frames. This study has developed and evaluated FetalSegNet, a multi-label deep learning framework that can simultaneously perform segmentation of the fetus and selected sub-anatomical structures, including the head, arms, legs and heart. Methods: A dataset comprising 3321 prenatal ultrasound images obtained from 217 patients was created, with pixel-level annotations generated under expert supervision. FetalSegNet was implemented using four hybrid encoder–decoder configurations that combine EfficientNetV2-M and MaxViT-S encoders with UNet++ and Feature Pyramid Network (FPN) decoders. Performance was evaluated on an unseen test set using Dice score, intersection over union (IoU), precision, recall, 95th percentile Hausdorff distance (HD95) and average symmetric surface distance (ASSD). The presence of anatomical structures was also assessed using classification metrics, while HiResCAM was used to examine the spatial relevance of model activations. Results: Among the FetalSegNet configurations, the EfficientNetV2-M-UNet++ model achieved the highest overall segmentation performance, with a Dice (DSC) score of 0.9232, IoU of 0.8746, HD95 of 4.23 mm, and ASSD of 1.02 mm. Class-specific results indicated better performance in segmentation for the head and fetus, whereas the leg and heart classes showed comparatively poorer and more inconsistent results. The models also achieved high F1-scores and a high level of accuracy in assessing the presence of anatomical structures. HiResCAM analyses showed that model activations were predominantly concentrated within the corresponding anatomical regions. Conclusions: FetalSegNet provides a unified, multi-label framework for simultaneously segmenting the fetus and multiple sub-anatomical structures in prenatal ultrasound images. By combining a hybrid deep-learning architecture with independent output-mask channels, comprehensive boundary-based evaluation, and activation-based interpretability analysis, the framework offers an integrated strategy for fetal anatomical segmentation. These findings support further investigation of FetalSegNet for automated and interpretable analysis of prenatal ultrasound images.