Precise Segmentation of Eye Muscle Area in Sheep Based on Multi-Scale Feature Fusion and Attention Mechanism

In modern animal husbandry, the eye muscle area is a key meat quality indicator, influencing consumer choices. Traditionally, measuring this area involves post-slaughter manual tracing, which is time-consuming and labor-intensive. In this work, due to a lack of public animal ultrasound datasets, this study established the EM-Sheep dataset for automated eye muscle in sheep using ultrasound images. We propose an advanced ultrasound image segmentation algorithm, MOC-YOLO, based on multi-scale feature fusion and attention mechanisms. This algorithm builds upon YOLOv8 as the base network and integrates the MOCA module to enhance multi-scale feature extraction and address complex boundary recognition in the segmentation of the eye muscle area. Additionally, the C2f-CGA module addresses noise, low contrast, and artifacts in B-mode ultrasound images. Experimental results show that MOC-YOLO significantly improves segmentation performance on the EM-Sheep dataset, with a 2.4% increase in IoU, 7.2% in accuracy, 3.9% in Dice and F1 scores, and boosts in Precision and Recall by 2.4% and 5.3%, respectively.