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@article{162768,
        author = {Dr. P. Ammireddy and G. Chiranjeevi and A. Showri Joseph Kumar and A. Siva Kumar and B. Jeevan Harsha},
        title = {DEEP LEARNING APPROACH FOR TOOTH  INSTANCE SEGMENTATION ON PANORAMIC  DENTAL RADIOGRAPHS USING U-NET},
        journal = {International Journal of Innovative Research in Technology},
        year = {},
        volume = {10},
        number = {10},
        pages = {959-963},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=162768},
        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.
},
        keywords = {Dental, Panoramic, Segmentation, Counting, Deep learning, U-net.},
        month = {},
        }