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Quantitative salivary gland SPECT/CT using deep convolutional neural networks.

Junyoung Park1,2, Jae Sung Lee1,2,3, Dongkyu Oh2,4

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A new deep learning method automates salivary gland segmentation for quantitative SPECT/CT scans, improving accuracy and efficiency. This advanced technique enhances salivary gland function evaluation while reducing patient radiation exposure.

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Area of Science:

  • Nuclear Medicine
  • Medical Imaging
  • Artificial Intelligence

Background:

  • Quantitative salivary gland SPECT/CT is crucial for assessing function but faces segmentation challenges.
  • Manual segmentation is time-consuming, requiring approximately 15 minutes per scan.

Purpose of the Study:

  • To develop and validate an automated salivary gland segmentation method using a deep convolutional neural network (CNN).
  • To improve the efficiency and accuracy of quantitative salivary gland SPECT/CT analysis.

Main Methods:

  • A modified 3D U-Net CNN was trained on 333 human expert segmentations for end-to-end learning.
  • The protocol involved SPECT/CT at 20 min and SPECT at 40 min post-Tc-99m pertechnetate injection, with sialagogue stimulation.
  • Automated segmentation performance was compared to manual segmentation using Dice similarity coefficient and %injected dose (%ID) correlation.

Main Results:

  • The automated method achieved high reproducibility, comparable to human experts (Dice: 0.81 parotid, 0.79 submandibular).
  • Excellent correlation was observed between automated and manual %ID quantitation (R²=0.93 parotid, R²=0.95 submandibular).
  • Analysis time was significantly reduced to less than 1 minute per scan.

Conclusions:

  • A CNN-aided quantitative salivary gland SPECT/CT protocol is feasible for clinical use.
  • The automated method offers substantial savings in time and manual effort.
  • This approach has the potential to reduce patient radiation exposure through faster scan times.