Precise segmentation of fetal head in ultrasound images using improved U‐Net model.

Monitoring fetal growth in utero is crucial to anomaly diagnosis. However, current computer‐vision models struggle to accurately assess the key metrics (i.e., head circumference and occipitofrontal and biparietal diameters) from ultrasound images, largely owing to a lack of training data. Mitigation usually entails image augmentation (e.g., flipping, rotating, scaling, and translating). Nevertheless, the accuracy of our task remains insufficient. Hence, we offer a U‐Net fetal head measurement tool that leverages a hybrid Dice and binary cross‐entropy loss to compute the similarity between actual and predicted segmented regions. Ellipse‐fitted two‐dimensional ultrasound images acquired from the HC18 dataset are input, and their lower feature layers are reused for efficiency. During regression, a novel region of interest pooling layer extracts elliptical feature maps, and during segmentation, feature pyramids fuse field‐layer data with a new scale attention method to reduce noise. Performance is measured by Dice similarity, mean pixel accuracy, and mean intersection‐over‐union, giving 97.90%, 99.18%, and 97.81% scores, respectively, which match or outperform the best U‐Net models. [ABSTRACT FROM AUTHOR]