MCPNet: Morphological Constraint‐Based Copy‐Paste Network for Semi‐Supervised Foetal Head Segmentation

BACKGROUND: The foetal head's automatic segmentation from ultrasound imagery is considered a key step in prenatal examination. However, achieving high-quality semi-supervised foetal head image segmentation remains challenging due to low image resolution, unclear boundaries, and inconsistencies between labelled and unlabelled data. METHODS: To overcome these obstacles, we propose MCPNet, a morphological constraint-based copy-paste network for semi-supervised foetal head segmentation, incorporating score-guided morphological refinement (SMR) and copy-paste mixing augmentation (CPMA). SMR employs weighted scores derived from Sobel operators and Euclidean transform to ensure boundary consistency. Additionally, to mitigate the distribution gap between labelled and unlabelled data, we introduce CPMA. This method uses random cropping to swap foreground and background between labelled and unlabelled data. RESULTS: On the HC18 and PSFH benchmarks, our method achieves Dice scores of 93.72% and 92.31% respectively with 20% labelled data. CONCLUSIONS: The results demonstrate our superior performance and clinical potential.