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Deep Learning-Assisted Diffusion Tensor Imaging for Evaluation of the Physis and Metaphysis

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Abstract

Diffusion tensor imaging of physis and metaphysis can be used as a biomarker to predict height change in the pediatric population. Current application of this technique requires manual segmentation of the physis which is time-consuming and introduces interobserver variability. UNET Transformers (UNETR) can be used for automatic segmentation to optimize workflow. Three hundred and eighty-five DTI scans from 191 subjects with mean age of 12.6 years ± 2.01 years were retrospectively used for training and validation. The mean Dice correlation coefficient was 0.81 for the UNETR model and 0.68 for the UNET. Manual extraction and segmentation took 15 min per volume, whereas both deep learning segmentation techniques took < 1 s per volume and were deterministic, always producing the same result for a given input. Intraclass correlation coefficient (ICC) for ROI-derived femur diffusion metrics was excellent for tract count (0.95), volume (0.95), and FA (0.97), and good for tract length (0.87). The results support the hypothesis that a hybrid UNETR model can be trained to replace the manual segmentation of physeal DTI images, therefore automating the process.

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Data Availability

The datasets generated and analyzed for this study are not openly available as they contained protected health information. Deidentified data is available from the corresponding author upon reasonable request.

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Funding

D.J. grants are from the National Institutes of Health and Society for Pediatric Radiology Research and Education Fund, supported by the National Institutes of Health (R01 HD104720 [D.J.].

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Authors and Affiliations

  1. Department of Radiology, Columbia University Irving Medical Center, New York, NY, USA

    Phuong T. Duong, Laura Santos, Hao-Yun Hsu, Sachin Jambawalikar & Diego Jaramillo

  2. Lenox Hill Radiology, New York, NY, USA

    Simukayi Mutasa

  3. Sidney Kimmel Medical College at Thomas Jefferson University, Philadelphia, PA, USA

    Michael K. Nguyen

  4. Children’s Hospital of Philadelphia, Philadelphia, PA, USA

    Andressa Guariento

Authors
  1. Phuong T. Duong
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  2. Laura Santos
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  3. Hao-Yun Hsu
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  4. Sachin Jambawalikar
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  5. Simukayi Mutasa
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  6. Michael K. Nguyen
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  8. Diego Jaramillo
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Contributions

Guarantors of integrity of entire study, D.J., P.T.D., L.S., H.Y.H; study concepts/study design or data acquisition or data analysis/interpretation, all authors; manuscript drafting or manuscript revision for important intellectual content, all authors; approval of final version of submitted manuscript, all authors; agrees to ensure any questions related to the work are appropriately resolved, all authors; literature research, D.J., P.T.D., L.S., H.Y.H.; clinical studies, P.T.D., L.S., H.Y.H; statistical analysis, L.S. and H.Y.H.; and manuscript editing, all authors.

Corresponding author

Correspondence to Phuong T. Duong.

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Duong, P.T., Santos, L., Hsu, HY. et al. Deep Learning-Assisted Diffusion Tensor Imaging for Evaluation of the Physis and Metaphysis. J Digit Imaging. Inform. med. 37, 756–765 (2024). https://doi.org/10.1007/s10278-024-00993-3

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