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Automatic MRI–based rotator cuff muscle segmentation using U-Nets

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Abstract

Background

The rotator cuff (RC) is a crucial anatomical element within the shoulder joint, facilitating an extensive array of motions while maintaining joint stability. Comprised of the subscapularis, infraspinatus, supraspinatus, and teres minor muscles, the RC plays an integral role in shoulder functionality. RC injuries represent prevalent, incapacitating conditions that impose a substantial impact on approximately 8% of the adult population in the USA. Segmentation of these muscles provides valuable anatomical information for evaluating muscle quality and allows for better treatment planning.

Materials and methods

We developed a model based on residual deep convolutional encoder-decoder U-net to segment RC muscles on oblique sagittal T1-weighted images MRI. Our data consisted of shoulder MRIs from a cohort of 157 individuals, consisting of individuals without RC tendon tear (N=79) and patients with partial RC tendon tear (N=78). We evaluated different modeling approaches. The performance of the models was evaluated by calculating the Dice coefficient on the hold out test set.

Results

The best-performing model’s median Dice coefficient was measured to be 89% (Q1:85%, Q3:96%) for the supraspinatus, 86% (Q1:82%, Q3:88%) for the subscapularis, 86% (Q1:82%, Q3:90%) for the infraspinatus, and 78% (Q1:70%, Q3:81%) for the teres minor muscle, indicating a satisfactory level of accuracy in the model’s predictions.

Conclusion

Our computational models demonstrated the capability to delineate RC muscles with a level of precision akin to that of experienced radiologists. As hypothesized, the proposed algorithm exhibited superior performance when segmenting muscles with well-defined boundaries, including the supraspinatus, subscapularis, and infraspinatus muscles.

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Authors and Affiliations

  1. Department of Radiology, Division of Musculoskeletal Imaging and Intervention, University of Washington, UW Radiology-Roosevelt Clinic, 4245 Roosevelt Way NE, Box, Seattle, WA, 354755, USA

    Ehsan Alipour, Majid Chalian, Atefe Pooyan, Arash Azhideh & Firoozeh Shomal Zadeh

  2. Department of Biomedical Informatics and Medical Education, University of Washington, Seattle, WA, USA

    Ehsan Alipour

  3. Department of Radiology, University of Washington, Seattle, WA, USA

    Hesamoddin Jahanian

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  1. Ehsan Alipour
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Correspondence to Majid Chalian.

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Conflict of interest

Majid Chalian is recipient of the RSNA R&E Scholar Grant and Boeing Technology Development Grant. The remaining authors declare no competing interests.

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Alipour, E., Chalian, M., Pooyan, A. et al. Automatic MRI–based rotator cuff muscle segmentation using U-Nets. Skeletal Radiol 53, 537–545 (2024). https://doi.org/10.1007/s00256-023-04447-9

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  • DOI: https://doi.org/10.1007/s00256-023-04447-9

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