To develop an intelligent model for uterine anatomical structure detection and segmentation (IMSU) by integrating the Efficient Multi-scale Attention (EMA) mechanism into the You Only Look Once version 8 (YOLOv8) framework and evaluate its performance.

 A total of 4326 non-pregnant mid-sagittal uterine ultrasound images were retrospectively collected from Shenzhen Maternity and Child Healthcare Hospital (January 2021-December 2022). Three key anatomical structures (uterine corpus, cervix, and endometrium) were manually annotated to establish an image database. The dataset was divided into training (3460 images), validation (433 images), and test sets (424 images) at an 8∶1∶1 ratio. An IMSU model was constructed by enhancing YOLOv8 with the EMA module. Both the baseline YOLOv8 and IMSU models were trained and validated, followed by performance evaluation on the test set for automated detection and segmentation of uterine structures. Metrics included precision, recall, and mean Average Precision (mAP) at two levels: mAP@50 and mAP@50-95.

 For detection tasks, IMSU outperformed YOLOv8 in overall precision (0.920 vs 0.905), recall (0.939 vs 0.917), and mAP@50 (0.952 vs 0.933). Notably, cervical detection mAP@50 improved from 0.858 to 0.919 and recall increased from 0.778 to 0.842. In segmentation tasks, IMSU achieved higher precision (0.914 vs 0.905), recall (0.933 vs 0.915), mAP@50 (0.952 vs 0.930), and mAP@50-95 (0.677 vs 0.661). Cervical segmentation mAP@50-95 rose from 0.570 to 0.597.

 The EMA-enhanced IMSU significantly improves automated detection and segmentation accuracy for key uterine structures in mid-sagittal ultrasound images, providing technical support for intelligent quantitative uterine measurements and ultrasound-assisted diagnosis with promising clinical applicability.