A decoupled generative adversarial network for anterior cruciate ligament tear localization and quantification

The anterior cruciate ligament (ACL) is one of the most commonly injured ligaments in the knee. Accurate tear quantification of ACL plays a crucial role in the treatment of patients. This study aims to propose an auxiliary diagnosis scheme based on deep learning (DL) to assist orthopedic surgeons in automatic ACL tear localization and quantification. The proposed scheme adopted a decoupled generative adversarial network (GAN) to generate the distal residual mask and the normal ACL mask, thereby achieving ACL tear classification. Since the edge information of ACL is important in tear classification, we built the decoupled GAN by decoupling the body and edge parts of masks with different supervision and improved its segmentation performance through a histogram equalization for enhancing image quality, an atrous spatial pyramid pooling (ASPP) module and a distribution module for improving feature representations as well as an effective channel attention mechanism. The experiments showed that the decoupled GAN model achieved promising results in the test set and demonstrated its feasibility in ACL segmentation. The proposed scheme also achieved good results in ACL tear quantification (accuracy: 0.929) and yielded a comparable performance with a senior orthopedic surgeon. This work can provide valuable diagnosis evidence of ACL tear for orthopedic surgeons and is expected to apply in clinical practice.