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Quantifying Knee Cartilage Shape and Lesion: From Image to Metrics

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Applications of Medical Artificial Intelligence (AMAI 2024)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 15384))

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Abstract

Imaging features of knee articular cartilage have been shown to be potential imaging biomarkers for knee osteoarthritis. Despite recent methodological advancements in image analysis techniques like image segmentation, registration, and domain-specific image computing algorithms, only a few works focus on building fully automated pipelines for imaging feature extraction. In this study, we developed a deep-learning-based medical image analysis application for knee cartilage morphometrics, CartiMorph Toolbox (CMT). We proposed a 2-stage joint template learning and registration network, CMT-reg. We trained the model using the OAI-ZIB dataset and assessed its performance in template-to-image registration. The CMT-reg demonstrated competitive results compared to other state-of-the-art models. We integrated the proposed model into an automated pipeline for the quantification of cartilage shape and lesion (full-thickness cartilage loss, specifically). The toolbox provides a comprehensive, user-friendly solution for medical image analysis and data visualization. The software and models are available at https://github.com/YongchengYAO/CMT-AMAI24paper.

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Notes

  1. 1.

    https://github.com/YongchengYAO/CMT-AMAI24paper.

  2. 2.

    Data from https://github.com/YongchengYAO/CartiMorph.

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Acknowledgments

This work was supported by the United Kingdom Research and Innovation (grant EP/S02431X/1), UKRI Centre for Doctoral Training in Biomedical AI at the University of Edinburgh, School of Informatics. For the purpose of open access, the author has applied a creative commons attribution (CC BY) licence to any author accepted manuscript version arising.

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Authors and Affiliations

  1. UKRI CDT in Biomedical AI, School of Informatics, The University of Edinburgh, Edinburgh, UK

    Yongcheng Yao

  2. CU Lab of AI in Radiology (CLAIR), Department of Imaging and Interventional Radiology, The Chinese University of Hong Kong, Hong Kong, China

    Weitian Chen

Authors
  1. Yongcheng Yao
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  2. Weitian Chen
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Correspondence to Yongcheng Yao .

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Editors and Affiliations

  1. University of Pittsburgh, Pittsburgh, PA, USA

    Shandong Wu

  2. National Institute of Biomedical Imaging and Bioengineering, Bethesda, MD, USA

    Behrouz Shabestari

  3. Stanford University, Stanford, CA, USA

    Lei Xing

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Yao, Y., Chen, W. (2025). Quantifying Knee Cartilage Shape and Lesion: From Image to Metrics. In: Wu, S., Shabestari, B., Xing, L. (eds) Applications of Medical Artificial Intelligence. AMAI 2024. Lecture Notes in Computer Science, vol 15384. Springer, Cham. https://doi.org/10.1007/978-3-031-82007-6_16

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  • DOI: https://doi.org/10.1007/978-3-031-82007-6_16

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