Accurate measurement of fetal head circumference (HC) in ultrasound images remains essential yet challenging for obstetric assessment, primarily due to anatomical variations across gestational stages and inherent imaging artifacts. In response to these limitations, we introduce YOSAM, a novel framework for fetal HC measurement that synergistically combines YOLOv11-based detection with our enhanced MedSAM-AD model. The MedSAM-AD integrates an Adapter layer for domain-specific feature adaptation and a Dimensional Reciprocal Attention Mixing Transformer (D-RAMiT) block for a joint spatial-channel attention mechanism into the MedSAM architecture. Within our cascaded framework, YOLOv11 first generates bounding boxes to localize the fetal head, serving as spatial prompts for MedSAM-AD to perform precise segmentation. The segmented fetal head is then processed with Canny edge detection and elliptical fitting to compute HC. Experimental results show that our approach achieves outstanding performance among standard biometric metrics of the HC18 dataset, attaining a Dice Similarity Coefficient (DSC) of 98.06 ± 1.06%, a Difference (DF) of 0.13 ± 2.47 mm, an Absolute Difference (AD) of 1.76 ± 1.74 mm, and a Hausdorff Distance (HD) of 1.18 ± 0.71 mm. With HD as the principal criterion for boundary delineation, our method achieves state-of-the-art performance in fetal head boundary delineation.