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Automatic pterygopalatine fossa segmentation and localisation based on DenseASPP.

Bing Wang1, Weili Shi1

  • 1School of Computer Science and Technology, Changchun University of Science and Technology, Changchun, Jilin, China.

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Summary

A novel deep learning model precisely segments the pterygopalatine fossa, improving safety and effectiveness for acupuncture in allergic rhinitis treatment. This AI-driven approach enhances anatomical targeting for better patient outcomes.

Keywords:
3D segmentationDenseASPPdeep learningpterygopalatine fossa

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Area of Science:

  • Medical Imaging
  • Artificial Intelligence in Medicine
  • Anatomy

Background:

  • Allergic rhinitis is a common condition with suboptimal traditional treatments.
  • Pterygopalatine fossa acupuncture is effective but anatomically challenging.
  • Current methods lack precision and safety for targeting the pterygopalatine fossa.

Purpose of the Study:

  • To develop a deep learning model for precise pterygopalatine fossa segmentation.
  • To enhance the safety and accuracy of pterygopalatine fossa-assisted procedures.
  • To improve the localization of the pterygopalatine fossa for therapeutic interventions.

Main Methods:

  • A deep learning model based on the U-Net framework was developed.
  • DenseASPP and an attention mechanism were integrated for enhanced segmentation.
  • The model was trained to refine the segmentation of the pterygopalatine fossa.

Main Results:

  • The model achieved a Dice Similarity Coefficient of 93.89%.
  • A Hausdorff Distance of 2.53 mm demonstrated high precision.
  • The model utilizes only 1.98 million parameters, indicating efficiency.

Conclusions:

  • Deep learning significantly advances pterygopalatine fossa localization and segmentation.
  • The model provides a reliable tool for guiding pterygopalatine fossa-assisted punctures.
  • This AI approach offers a safer and more precise alternative for treating allergic rhinitis.