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Comparing bone shape models from deep learning processing of magnetic resonance imaging to computed tomography-based

Victoria Wong1, Francesco Calivá1, Favian Su2

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Summary

This study introduces a deep learning method for automatic scapular bone segmentation in MRI scans, achieving high accuracy comparable to CT scans for clinical measurements.

Keywords:
Artificial intelligenceCTDeep learningMRIMachine learningScapula

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Area of Science:

  • Medical Imaging
  • Artificial Intelligence
  • Orthopedics

Background:

  • Shoulder pathology diagnosis often requires multiple imaging modalities.
  • Accurate scapular bone segmentation is crucial for assessing shoulder joint health.
  • Current segmentation methods can be time-consuming and operator-dependent.

Purpose of the Study:

  • To develop a deep learning approach for automatic scapular bone segmentation on MRI.
  • To compare the accuracy of 3D MRI-based models with 3D CT scans.

Main Methods:

  • A convolutional neural network approach was used to train 2D and 3D models.
  • Manual segmentation by researchers served as the ground truth.
  • Model performance was evaluated using the Dice Similarity Coefficient (DSC) and compared between MRI and CT data.

Main Results:

  • Both 2D and 3D deep learning models achieved high DSC scores (0.86 and 0.82, respectively).
  • Image data augmentation improved 3D model performance.
  • Clinical measurements derived from 3D MRI showed minimal differences compared to CT.

Conclusions:

  • A fully automatic deep learning strategy for scapular shape extraction from MRI was developed.
  • This technology can potentially reduce the need for multiple imaging studies.
  • Surgeons may obtain comprehensive clinical information from MRI scans alone.