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Development of an Open-Source Algorithm for Automated Segmentation in Clinician-Led Paranasal Sinus Radiologic

Rhea Darbari Kaul1,2,3, Wenjin Zhong4, Sidong Liu4

  • 1Rhinology and Skull Base Research Group, Applied Medical Research Centre, University of New South Wales, Sydney, Australia.

The Laryngoscope
|May 27, 2025
PubMed
Summary
This summary is machine-generated.

An open-source deep learning algorithm for segmenting paranasal sinus CT scans demonstrates good accuracy. This tool supports clinician-led AI research in otolaryngology by providing accessible computational expertise.

Keywords:
CTartificial intelligenceautomated segmentationradiomicsrhinology

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Area of Science:

  • Medical Imaging
  • Artificial Intelligence
  • Otolaryngology

Background:

  • Clinician-led Artificial Intelligence (AI) research requires accessible computational tools.
  • Existing AI segmentation algorithms for paranasal sinus computed tomography (CT) are often not openly accessible.
  • There is a need for validated, open-source algorithms to facilitate AI-driven medical research in otolaryngology.

Purpose of the Study:

  • To validate and provide an open-source deep learning segmentation algorithm for paranasal sinus CT scans.
  • To support the otolaryngology research community with clinically driven AI tools.
  • To enable more complex AI-based analysis of large medical datasets.

Main Methods:

  • A UNet++ deep learning algorithm was modified for automatic segmentation of paranasal sinus CTs.
  • A dataset of 100 paranasal sinus CT scans was manually segmented and split for training (80%) and testing (20%).
  • Performance was evaluated using Dice Similarity Coefficient (DSC), Intersection over Union (IoU), Hausdorff distance (HD), sensitivity, specificity, and visual grading.

Main Results:

  • The algorithm achieved a mean DSC of 0.87 and IoU of 0.80.
  • Mean sensitivity was 83.98% and specificity was 99.81%, with a mean HD of 33.61 mm.
  • Visual similarity grading was a median of 3 (good), with no significant differences between normal and diseased scans.

Conclusions:

  • Automatic segmentation of paranasal sinus CTs using deep learning shows promising results compared to manual segmentation.
  • The study provides a validated, open-source segmentation algorithm for the otolaryngology research community.
  • This algorithm serves as a foundation for advanced AI applications in analyzing sinonasal imaging data.