Abstract: Objective: To compare different algorithms for TMJ CBCT image segmentation and explore an ideal optimization segmentation algorithm. Methods: The CBCT images of 46 TMJ-OA condyles and 46 healthy condyles were included and three-dimensional model was reconstructed. The images of condyles were segmented by the manual segmentation, threshold segmentation, and hybrid algorithm based on borderless active contour model and region growing. The methods were compared concerning time expenditure, adjustability, adaptability of segmentation contours, software interface friendliness score, condylar volume, and surface size. Results: No significant morphological differences were found between manual segmentation and hybrid algorithm segmentation of condyles with TMJ-OA. The volume of condyles with threshold segmentation was significantly smaller than that with the other methods. The value of volume discrepancy was 85.7±296.1mm. The hybrid algorithm segmentation was significantly faster with better usability compared with manual and threshold based segmentation method. Conclusion: The hybrid algorithm segmentation method based on the borderless active contour model and region growing algorithm is a reliable and intelligent approach for condyle contouring in patients with TMJ-OA, which is better than single threshold based segmentation.

Key words: Image segmentation, TMJ-OA, Borderless active contour model