Author Correction: Canal-Net for automatic and robust 3D segmentation of mandibular canals in CBCT images using a continuity-aware contextual network

Jeoun, Bo-Soung; Yang, Shi Wei; Lee, Sang Jeong; Kim, Tae Il; Kim, Jun-Min; Kim, Jo‐Eun; Huh, Kyung-Hoe; Lee, Sam-Sun; Heo, Min-Suk; Yi, Won-Jin

doi:10.1038/s41598-022-25677-2

Sci Rep

2022

DOI: 10.1038/s41598-022-25677-2

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Author Correction: Canal-Net for automatic and robust 3D segmentation of mandibular canals in CBCT images using a continuity-aware contextual network

Bo-Soung Jeoun

Shi Wei Yang

Sang Jeong Lee³

et al.

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A unique artificial intelligence-based tool for automated CBCT segmentation of mandibular incisive canal

Jindanil,

Marinho-Vieira,

de-Azevedo-Vaz

et al. 2023

Dentomaxillofacial Radiology

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Objectives: To develop and validate a novel artificial intelligence (AI) tool for automated segmentation of mandibular incisive canal on cone beam computed tomography (CBCT) scans. Methods: After ethical approval, a data set of 200 CBCT scans were selected and categorized into training (160), validation (20), and test (20) sets. CBCT scans were imported into Virtual Patient Creator and ground truth for training and validation were manually segmented by three oral radiologists in multiplanar reconstructions. Intra- and interobserver analysis for human segmentation variability was performed on 20% of the data set. Segmentations were imported into Mimics for standardization. Resulting files were imported to 3-Matic for analysis using surface- and voxel-based methods. Evaluation metrics involved time efficiency, analysis metrics including Dice Similarity Coefficient (DSC), Intersection over Union (IoU), Root mean square error (RMSE), precision, recall, accuracy, and consistency. These values were calculated considering AI-based segmentation and refined-AI segmentation compared to manual segmentation. Results: Average time for AI-based segmentation, refined-AI segmentation and manual segmentation was 00:10, 08:09, and 47:18 (284-fold time reduction). AI-based segmentation showed mean values of DSC 0.873, IoU 0.775, RMSE 0.256 mm, precision 0.837 and recall 0.890 while refined-AI segmentation provided DSC 0.876, IoU 0.781, RMSE 0.267 mm, precision 0. 852 and recall 0.902 with the accuracy of 0.998 for both methods. The consistency was one for AI-based segmentation and 0.910 for manual segmentation. Conclusions: An innovative AI-tool for automated segmentation of mandibular incisive canal on CBCT scans was proofed to be accurate, time efficient, and highly consistent, serving pre-surgical planning.

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A unique artificial intelligence-based tool for automated CBCT segmentation of mandibular incisive canal

Jindanil,

Marinho-Vieira,

de-Azevedo-Vaz

et al. 2023

Dentomaxillofacial Radiology

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show abstract

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Author Correction: Canal-Net for automatic and robust 3D segmentation of mandibular canals in CBCT images using a continuity-aware contextual network

Cited by 1 publication

References 0 publications

A unique artificial intelligence-based tool for automated CBCT segmentation of mandibular incisive canal

A unique artificial intelligence-based tool for automated CBCT segmentation of mandibular incisive canal

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