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

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Abstract

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.

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Data availability

The dataset analyzed during the current study are not publicly available due to data privacy but are available from the corresponding author on reasonable request.

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Acknowledgements

This work was supported by the Natural Science Foundation of Hunan Province China under Grants 2022JJ30673; National Natural Science Foundation of China (No. 81802208) and the Foundation of Health Commission of Hunan Province (No. 202204074821); the Wisdom Accumulation and Talent Cultivation Project of the Third Xiangya Hospital of Central South University (YX202209) fund this study.

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  1. Jiaoju Wang and Jiewen Luo have contributed equally to this work.

Authors and Affiliations

  1. School of Mathematics and Statistics, Central South University, Changsha, 410083, Hunan, China

    Jiaoju Wang, Alphonse Houssou Hounye, Menglin Kong & Muzhou Hou

  2. Department of Orthopaedic Surgery, Third Xiangya Hospital of Central South University, Changsha, 410013, Hunan, China

    Jiewen Luo, Jiehui Liang, Yangbo Cao, Lingjie Tan & Jinshen He

  3. School of Computer Science, Hunan First Normal University, Changsha, 410083, Hunan, China

    Zheng Wang

  4. Department of Orthopaedic Surgery, Changsha Central Hospital of South China University, Changsha, 410004, Hunan, China

    Jing Feng

  5. Department of Orthopaedic Surgery, People’s Hospital of Ningxia Hui Autonomous Region, Yinchuan, Ningxia Hui Autonomous Region, 750021, China

    Zhengcheng Wang

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  1. Jiaoju Wang
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Contributions

JW and JL performed conceptualization, methodology, software, formal analysis, and writing—original draft. AHH and ZW provide writing—review and editing and optimation of methodology. JL, YC, JF, LT, and ZW gave clinical validation. MH and JH did financial support, investigation, resources, and writing-review and editing.

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Correspondence to Muzhou Hou or Jinshen He.

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Wang, J., Luo, J., Hounye, A.H. et al. A decoupled generative adversarial network for anterior cruciate ligament tear localization and quantification. Neural Comput & Applic 35, 19351–19364 (2023). https://doi.org/10.1007/s00521-023-08776-7

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  • DOI: https://doi.org/10.1007/s00521-023-08776-7

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