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A cartilage injury segmentation algorithm based on subordinate degree analysis during lesion location-directed imitation

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Abstract

To accomplish early detection of cartilage injury, avert the ultimate development of degenerative necrosis, and cause permanent harm, MRI images clearly show chondrolesions and overcome the irreversible damage caused by minimally invasive surgery. A cartilage injury segmentation algorithm based on subordinate degree analysis (SDA-LD) is proposed, which can effectively assist clinicians in conducting early diagnosis and determine follow-up plans. On the one hand, to reduce the attention on the non-essential region in knee cartilage images, the location-directed imitation mechanism is required by mimicking the attention allocation process. On the other hand, the proposed SDA-LD method leverages the subordinate degree analysis matrix to determine the association between rich global cartilage information and local lesion texture features. Meanwhile, it properly achieves the correlation between regions of the shape-irregular region of interest (ROI). The experiment operates in the same environment to fairly measure the performance of the proposed algorithm and successfully generates the appropriate quantitative or qualitative analysis by contrasting it with state-of-the-art segmentation algorithms. Experiments show that the proposed SDA-LD algorithm can achieve dice, Jaccard, and recall values of 0.95, 0.90, and 0.95 on real-world medical cartilage injury datasets, respectively.

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Availability of data and materials

The datasets generated during and analyzed during the current study are available from the corresponding author on reasonable request.

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Funding

This work was supported by China Postdoctoral Science Foundation under Grant 2021M700676, Natural Science Foundation of Liaoning Province under Grant 2021-MS-272, and Dalian high-level Talents Innovation plan under Grant 2019RQ021.

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Authors and Affiliations

  1. Department of Computing and Information Technology, Liaoning Normal University, Dalian City, Liaoning Province, China

    Lingling Fang, Xin Wang, Huan Qiao & Deshan Liu

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  1. Lingling Fang
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  2. Xin Wang
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  3. Huan Qiao
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Contributions

XW wrote the main manuscript, and LF overall controlled, and HQ revised the paper. After modification, DL checks the paper as a whole. All authors reviewed the manuscript.

Corresponding author

Correspondence to Deshan Liu.

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Competing interests

We would like to submit the enclosed manuscript entitled "A cartilage injury segmentation algorithm based on subordinate degree analysis during lesion location-directed imitation," which we wish to be considered for publication in the "Signal, Image and Video Processing" journal. All co-authors have seen and agree with the contents of the manuscript, and there is no financial interest to report. We certify that the submission is original work and is not under review at any other publication.

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We avoid distorting the research results and refuse to do anything that harms the trust of journals, the professionalism of scientific authors, and the ultimate scientific efforts. Ensuring that the rules of good scientific practice are followed is conducive to maintaining the integrity of research and its statements.

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Fang, L., Wang, X., Qiao, H. et al. A cartilage injury segmentation algorithm based on subordinate degree analysis during lesion location-directed imitation. SIViP 17, 4367–4374 (2023). https://doi.org/10.1007/s11760-023-02669-x

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  • DOI: https://doi.org/10.1007/s11760-023-02669-x

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