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Related Experiment Video

Updated: Oct 10, 2025

A Morphometric and Cellular Analysis Method for the Murine Mandibular Condyle

A Morphometric and Cellular Analysis Method for the Murine Mandibular Condyle

Published on: January 11, 2018

Automatic Segmentation of Mandibular Ramus and Condyles.

Celia Le, Romain Deleat-Besson, Juan Prieto

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference

|December 11, 2021

Summary

A new algorithm, MandSeg, uses AI to segment mandibular condyles and ramus from cone-beam CT scans, improving TMJ pathology diagnosis. This automated segmentation enables efficient analysis of large datasets for disease classification.

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Author Spotlight: Three-Dimensional Cephalometric Landmark Annotation Demonstration on Human Cone Beam Computed Tomography Scans

Author Spotlight: Three-Dimensional Cephalometric Landmark Annotation Demonstration on Human Cone Beam Computed Tomography Scans

Published on: September 8, 2023

Area of Science:

Medical Imaging
Artificial Intelligence
Biomedical Engineering

Background:

Diagnosing temporomandibular joint (TMJ) pathologies requires accurate analysis of anatomical structures.
Current segmentation methods for mandibular condyles and ramus can be time-consuming and subjective.
Cone-beam computed tomography (CBCT) is a common imaging modality for TMJ assessment.

Purpose of the Study:

To develop and validate MandSeg, a novel algorithm for automated segmentation of mandibular condyles and ramus from CBCT scans.
To improve the efficiency and accuracy of image analysis for TMJ pathology diagnosis.
To facilitate the extraction of radiomic and imaging features for objective diagnostic criteria.

Main Methods:

A deep neural network based on the U-Net architecture was trained using 109 CBCT scans.
Pre-processing involved cropping to the mandibular region of interest, resizing to 512x512, and contrast adjustment.
Approximately 350 slices per scan were used for training, with 10-fold cross-validation.

Main Results:

The MandSeg algorithm achieved high performance metrics: 0.95 ± 0.05 AUC, 0.93 ± 0.06 sensitivity, 0.9998 ± 0.0001 specificity, 0.9996 ± 0.0003 accuracy, and 0.91 ± 0.03 F1 score.
The algorithm demonstrated effective segmentation across scans from different centers and resolutions.
The study confirmed fast and efficient segmentation capabilities.

Conclusions:

MandSeg provides a robust and efficient solution for automated segmentation of mandibular condyles and ramus from CBCT images.
This automated approach supports the analysis of large datasets for TMJ pathology classification.
The developed segmentation method paves the way for utilizing radiomic features in diagnosing TMJ disorders like osteoarthritis (OA).