Abstract
Locating the hip joint center (HJC) from X-ray images is frequently required for the evaluation of hip dysplasia. Existing state-of-the-art methods focus on developing functional methods or regression equations with some radiographic landmarks. Such developments employ shallow networks or single equations to locate the HJC, and little attention has been given to deep stacked networks. In addition, existing methods ignore the connections between static and dynamic landmarks, and their prediction capacity is limited. This paper proposes an innovative hybrid framework for HJC identification. The proposed method is based on fast deep stacked network (FDSN) and dynamic registration graph with four improvements: (1) an anatomical landmark extraction module obtains comprehensive prominent bony landmarks from multipose X-ray images; (2) an attribute optimization module based on grey relational analysis (GRA) guides the network to focus on useful external anatomical landmarks; (3) a multiverse optimizer (MVO) module appended to the framework automatically and efficiently determines the optimal model parameters; and (4) the dynamic fitting and two-step registration approach are integrated into the model to further improve the accuracy of HJC localization. By integrating the above improvements in series, the models’ performances are gradually enhanced. Experimental results show that our model achieves superior results to existing HJC prediction approaches.
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Acknowledgments
This work was supported by the National Natural Science Foundation of China (No.61772556), National Key R&D Program of China (No.2018YFB1107100, No.2016 YFC1100600), Postgraduate Research and Innovation Project of Hunan (No.CX20200321) and Fundamental Research Funds for the Central Universities of Central South University (2020zzts140).
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Han, F., Liao, S., Wu, R., Liu, S., Zhao, Y., Shen, X. (2021). Radiological Identification of Hip Joint Centers from X-ray Images Using Fast Deep Stacked Network and Dynamic Registration Graph. In: Farkaš, I., Masulli, P., Otte, S., Wermter, S. (eds) Artificial Neural Networks and Machine Learning – ICANN 2021. ICANN 2021. Lecture Notes in Computer Science(), vol 12893. Springer, Cham. https://doi.org/10.1007/978-3-030-86365-4_52
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