Automatic Anterior Cruciate Ligament Insertion Center Positioning Method Framework for Isometric Reconstruction and...

Anterior cruciate ligament (ACL) reconstruction is a complex surgical procedure with high precision requirements. One of the most critical steps during the operation is the accurate localization of the insertion center and the proper placement of the graft. However, under limited visibility of the arthroscope, surgeons often face challenges in determining the correct position and angle. In this paper, we propose a novel framework for insertion center localization, incorporating two reconstruction approaches. First, an isometric reconstruction method based on 3D geometric processing algorithms is proposed, which leverages the feature lines and points of the geometric morphology of the actual bone model. This method ensures accurate alignment with bones of varying shapes. Second, a novel U-shaped network (EP-UNet), which integrates axial edge and coordinate features, is proposed for medical image segmentation to facilitate anatomical reconstruction. Experimental results demonstrate that EP-UNet exhibits superior performance in ACL segmentation tasks, achieving high accuracy and robustness. Compared to the baseline network, it improves the mean Intersection over Union (mIoU) by 8.16%, providing strong support for ligament image segmentation. This framework allows surgeons to efficiently and automatically determine the position of the patient's insertion center, addressing the challenge of clinical localization with high reliability and improving surgical success rates.