Towards clinically applicable automated mandibular canal segmentation on CBCT
Copyright © 2024 Elsevier Ltd. All rights reserved..
OBJECTIVES: To develop a deep learning-based system for precise, robust, and fully automated segmentation of the mandibular canal on cone beam computed tomography (CBCT) images.
METHODS: The system was developed on 536 CBCT scans (training set: 376, validation set: 80, testing set: 80) from one center and validated on an external dataset of 89 CBCT scans from 3 centers. Each scan was annotated using a multi-stage annotation method and refined by oral and maxillofacial radiologists. We proposed a three-step strategy for the mandibular canal segmentation: extraction of the region of interest based on 2D U-Net, global segmentation of the mandibular canal, and segmentation refinement based on 3D U-Net.
RESULTS: The system consistently achieved accurate mandibular canal segmentation in the internal set (Dice similarity coefficient [DSC], 0.952; intersection over union [IoU], 0.912; average symmetric surface distance [ASSD], 0.046 mm; 95% Hausdorff distance [HD95], 0.325 mm) and the external set (DSC, 0.960; IoU, 0.924; ASSD, 0.040 mm; HD95, 0.288 mm).
CONCLUSIONS: These results demonstrated the potential clinical application of this AI system in facilitating clinical workflows related to mandibular canal localization.
CLINICAL SIGNIFICANCE: Accurate delineation of the mandibular canal on CBCT images is critical for implant placement, mandibular third molar extraction, and orthognathic surgery. This AI system enables accurate segmentation across different models, which could contribute to more efficient and precise dental automation systems.
| Medienart: |
E-Artikel |
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| Erscheinungsjahr: |
2024 |
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| Erschienen: |
2024 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:144 |
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| Enthalten in: |
Journal of dentistry - 144(2024) vom: 01. Apr., Seite 104931 |
| Sprache: |
Englisch |
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| Beteiligte Personen: |
Ni, Fang-Duan [VerfasserIn] |
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| Links: |
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| Themen: |
Cone beam computed tomography |
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| Anmerkungen: |
Date Completed 24.04.2024 Date Revised 24.04.2024 published: Print-Electronic Citation Status MEDLINE |
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| doi: |
10.1016/j.jdent.2024.104931 |
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| funding: |
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| Förderinstitution / Projekttitel: |
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| PPN (Katalog-ID): |
NLM369481135 |
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| 500 | |a published: Print-Electronic | ||
| 500 | |a Citation Status MEDLINE | ||
| 520 | |a Copyright © 2024 Elsevier Ltd. All rights reserved. | ||
| 520 | |a OBJECTIVES: To develop a deep learning-based system for precise, robust, and fully automated segmentation of the mandibular canal on cone beam computed tomography (CBCT) images | ||
| 520 | |a METHODS: The system was developed on 536 CBCT scans (training set: 376, validation set: 80, testing set: 80) from one center and validated on an external dataset of 89 CBCT scans from 3 centers. Each scan was annotated using a multi-stage annotation method and refined by oral and maxillofacial radiologists. We proposed a three-step strategy for the mandibular canal segmentation: extraction of the region of interest based on 2D U-Net, global segmentation of the mandibular canal, and segmentation refinement based on 3D U-Net | ||
| 520 | |a RESULTS: The system consistently achieved accurate mandibular canal segmentation in the internal set (Dice similarity coefficient [DSC], 0.952; intersection over union [IoU], 0.912; average symmetric surface distance [ASSD], 0.046 mm; 95% Hausdorff distance [HD95], 0.325 mm) and the external set (DSC, 0.960; IoU, 0.924; ASSD, 0.040 mm; HD95, 0.288 mm) | ||
| 520 | |a CONCLUSIONS: These results demonstrated the potential clinical application of this AI system in facilitating clinical workflows related to mandibular canal localization | ||
| 520 | |a CLINICAL SIGNIFICANCE: Accurate delineation of the mandibular canal on CBCT images is critical for implant placement, mandibular third molar extraction, and orthognathic surgery. This AI system enables accurate segmentation across different models, which could contribute to more efficient and precise dental automation systems | ||
| 650 | 4 | |a Journal Article | |
| 650 | 4 | |a Research Support, Non-U.S. Gov't | |
| 650 | 4 | |a Cone beam computed tomography | |
| 650 | 4 | |a Convolutional neural networks | |
| 650 | 4 | |a Deep learning | |
| 650 | 4 | |a Inferior alveolar nerve | |
| 650 | 4 | |a Mandibular canal | |
| 700 | 1 | |a Xu, Zi-Neng |e verfasserin |4 aut | |
| 700 | 1 | |a Liu, Mu-Qing |e verfasserin |4 aut | |
| 700 | 1 | |a Zhang, Min-Juan |e verfasserin |4 aut | |
| 700 | 1 | |a Li, Shu |e verfasserin |4 aut | |
| 700 | 1 | |a Bai, Hai-Long |e verfasserin |4 aut | |
| 700 | 1 | |a Ding, Peng |e verfasserin |4 aut | |
| 700 | 1 | |a Fu, Kai-Yuan |e verfasserin |4 aut | |
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