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DEEP LEARNING APPROACH FOR TOOTH INSTANCE SEGMENTATION ON PANORAMIC DENTAL RADIOGRAPHS USING U-NET

  • Unique Paper ID: 162768
  • Volume: 10
  • Issue: 10
  • PageNo: 959-963
  • Abstract:
  • Dental radiography plays a critical role in diagnosing and treating oral health conditions, with panoramic dental radiographs being commonly used for comprehensive assessments. Automating the segmentation of individual teeth within panoramic radiographs is a crucial step towards improving diagnostic accuracy and efficiency. In this study, we explore a deep learning approach tailored specifically for panoramic dental radiographs, aiming to automatically segment teeth using the U-Net architecture. We propose leveraging the U-Net network to achieve precise tooth instance segmentation in panoramic X-ray images. The proposed method achieves an impressive Dice overlap score of 95.4% in overall teeth segmentation. What sets this approach apart is the introduction of a novel post-processing stage that refines the segmentation maps by applying grayscale morphological and filtering operations to the output of the U-Net network before binarization. The obtained results concludes Deep Learning approach along with innovative post-processing techniques, holds great promise for advancing image analysis in dentistry and beyond, offering potential applications to similar challenges in various domains.
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Cite This Article

  • ISSN: 2349-6002
  • Volume: 10
  • Issue: 10
  • PageNo: 959-963

DEEP LEARNING APPROACH FOR TOOTH INSTANCE SEGMENTATION ON PANORAMIC DENTAL RADIOGRAPHS USING U-NET

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Impact Factor
8.01 (Year 2024)
UGC Approved
Journal no 47859

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