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Related Experiment Video

Updated: Dec 6, 2025

A Morphometric and Cellular Analysis Method for the Murine Mandibular Condyle
08:07

A Morphometric and Cellular Analysis Method for the Murine Mandibular Condyle

Published on: January 11, 2018

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3D Auto-Segmentation of Mandibular Condyles.

Serge Brosset, Maxime Dumont, Jonas Bianchi

    Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
    |October 6, 2020
    PubMed
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    This study presents an automated method for segmenting temporomandibular joint (TMJ) condyles from cone beam CT scans. Accurate TMJ osteoarthritis diagnosis is improved by this efficient bone structure analysis.

    Area of Science:

    • Medical imaging
    • Radiology
    • Biomedical engineering

    Background:

    • Temporomandibular joint (TMJ) disorders can cause significant pain and are often diagnosed late.
    • Bone resorption in TMJ Osteoarthritis (OA) is detectable early, highlighting the need for advanced diagnostic tools.
    • Analyzing TMJ bone structure is crucial for early disease detection, but condyle segmentation is a key challenge.

    Purpose of the Study:

    • To develop an automated method for segmenting the temporomandibular joint condyle from cone beam CT (CBCT) scans.
    • To improve the efficiency of analyzing large datasets for TMJ disorder research.
    • To facilitate data science and machine learning approaches for disease classification.

    Main Methods:

    • Image denoising techniques were applied to CBCT scans.

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    Last Updated: Dec 6, 2025

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  • A combination of 3D active contour and morphological operations was used for condyle segmentation.
  • The method was validated on CBCT images from 95 patients.

    • Main Results:

    • The proposed method achieved a high Dice score of 0.9461 +/- 0.0888 for condyle segmentation.
    • The automated segmentation process demonstrated high accuracy and reliability.
    • The method enables efficient analysis of large CBCT scan datasets.

    Conclusions:

    • Automated TMJ condyle segmentation from CBCT scans is feasible and accurate.
    • This technique can significantly enhance the efficiency of large-scale data analysis for TMJ disorders.
    • The developed method supports advancements in data science and machine learning for TMJ OA classification.