Automatic deep learning segmentation of mandibular periodontal bone topography on cone-beam computed tomography images.
Authors
Affiliations (7)
Affiliations (7)
- Department of Periodontology, Semmelweis University, Szentkirályi utca 47. 4th floor, 1088 Budapest, Hungary; Department of Periodontology, University of Bern, Freiburgstrasse 7. 3010 Bern, Switzerland; Dent.AI Medical Imaging Ltd., Irinyi József utca 31, 1111 Budapest, Hungary.. Electronic address: [email protected].
- Department of Periodontology, Semmelweis University, Szentkirályi utca 47. 4th floor, 1088 Budapest, Hungary; Dent.AI Medical Imaging Ltd., Irinyi József utca 31, 1111 Budapest, Hungary.. Electronic address: [email protected].
- Dent.AI Medical Imaging Ltd., Irinyi József utca 31, 1111 Budapest, Hungary.; Empresa de Base Technológica Internacional de Canarias, S.L. (EBATINCA), Calle Triana, 60, Piso 3, Oficina B, 35002 Las Palmas De Gran Canaria, Spain. Electronic address: [email protected].
- Dent.AI Medical Imaging Ltd., Irinyi József utca 31, 1111 Budapest, Hungary.. Electronic address: [email protected].
- School of Biomedical Engineering & Imaging Sciences, King's College London, St Thomas' Campus, St Thomas' Hospital, Westminster Bridge Road, London SE1 7EH, UK. Electronic address: [email protected].
- Department of Periodontology, Semmelweis University, Szentkirályi utca 47. 4th floor, 1088 Budapest, Hungary; Dent.AI Medical Imaging Ltd., Irinyi József utca 31, 1111 Budapest, Hungary.. Electronic address: [email protected].
- Department of Periodontology, University of Bern, Freiburgstrasse 7. 3010 Bern, Switzerland. Electronic address: [email protected].
Abstract
This study evaluated the performance of a multi-stage Segmentation Residual Network (SegResNet)-based deep learning (DL) model for the automatic segmentation of cone-beam computed tomography (CBCT) images of patients with stage III and IV periodontitis. Seventy pre-processed CBCT scans from patients undergoing periodontal rehabilitation were used for training and validation. The model was tested on 10 CBCT scans independent from the training dataset by comparing results with semi-automatic (SA) segmentations. Segmentation accuracy was assessed using the Dice similarity coefficient (DSC), Intersection over Union (IoU), and Hausdorff distance 95<sup>th</sup> percentile (HD95). Linear periodontal measurements were performed on four tooth surfaces to assess the validity of the DL segmentation in the periodontal region. The DL model achieved a mean DSC of 0.9650 ± 0.0097, with an IoU of 0.9340 ± 0.0180 and HD95 of 0.4820 mm ± 0.1269 mm, showing strong agreement with SA segmentation. Linear measurements revealed high statistical correlations between the mesial, distal, and lingual surfaces, with intraclass correlation coefficients (ICC) of 0.9442 (p<0.0001), 0.9232 (p<0.0001), and 0.9598(p<0.0001), respectively, while buccal measurements revealed lower consistency, with an ICC of 0.7481 (p<0.0001). The DL method reduced the segmentation time by 47 times compared to the SA method. Acquired 3D models may enable precise treatment planning in cases where conventional diagnostic modalities are insufficient. However, the robustness of the model must be increased to improve its general reliability and consistency at the buccal aspect of the periodontal region. This study presents a DL model for the CBCT-based segmentation of periodontal defects, demonstrating high accuracy and a 47-fold time reduction compared to SA methods, thus improving the feasibility of 3D diagnostics for advanced periodontitis.