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Area of Science:

  • Medical Imaging
  • Artificial Intelligence in Medicine
  • Oncology

Background:

  • Current head and neck cancer surgical planning relies on both CT and MRI, necessitating complex image registration and exposing patients to radiation.
  • This dual-modality approach presents challenges in workflow efficiency and resource utilization.

Purpose of the Study:

  • To develop and validate a deep learning model for accurate bone segmentation directly from T1-weighted MRI scans.
  • To enable MRI-only virtual surgical planning, thereby streamlining the process for head and neck oncology.

Main Methods:

  • A deep neural network was trained using CT-derived segmentations (mandible, cranium, inferior alveolar nerve) as ground truth.
  • A dataset of 100 patients with paired CT and MRI scans was utilized, with MRI data preprocessed to match CT voxel size and alignment.
  • The model was trained on 80 cases and evaluated on 20 cases using Dice similarity coefficient, Intersection over Union (IoU), precision, and recall.

Main Results:

  • The deep learning model achieved high accuracy in bone segmentation, with a mean Dice score of 0.86 and IoU of 0.76.
  • Precision and recall were both 0.86, indicating robust performance.
  • Surface deviation analysis revealed minimal differences (median 0.21-0.30 mm) between CT- and MRI-derived bone models.

Conclusions:

  • Accurate, CT-comparable bone models can be generated from standard T1-weighted MRI scans using deep learning.
  • This validates the feasibility of an MRI-only approach for virtual surgical planning in head and neck oncology.
  • The findings support reduced radiation exposure and improved efficiency in surgical planning workflows.