دورية أكاديمية
DentalSegmentator: Robust open source deep learning-based CT and CBCT image segmentation.
| العنوان: | DentalSegmentator: Robust open source deep learning-based CT and CBCT image segmentation. |
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| المؤلفون: | Dot G; UFR Odontologie, Universite Paris Cité, Paris, France; Service de Medecine Bucco-Dentaire, AP-HP, Hopital Pitie-Salpetriere, Paris, France; Institut de Biomecanique Humaine Georges Charpak, Arts et Metiers Institute of Technology, Paris, France. Electronic address: gauthier.dot@aphp.fr., Chaurasia A; Department of Oral Medicine and Radiology, Faculty of Dental Sciences, King George Medical University, Lucknow, Uttar Pradesh, India., Dubois G; Institut de Biomecanique Humaine Georges Charpak, Arts et Metiers Institute of Technology, Paris, France; Materialise France, Malakoff, France., Savoldelli C; Department of Oral and Maxillofacial Surgery, Head and Neck Institute, University Hospital of Nice, France., Haghighat S; Topic Group Dental Diagnostics and Digital Dentistry, ITU/WHO Focus Group AI On Health, Berlin, Germany., Azimian S; Research Committee, School of Dentistry, Shahid Beheshti University of Medical Sciences, Tehran, Iran., Taramsari AR; Private Dentist Practitioner, Rasht, Iran., Sivaramakrishnan G; Speciality Dental Residency Program, Primary Health Care Centers, Bahrain., Issa J; Department of Diagnostics, Chair of Practical Clinical Dentistry, Poznan University of Medical Sciences, Poznan, Poland; Doctoral School, Poznan University of Medical Sciences, Poznan, Poland., Dubey A; Department of Oral Medicine and Radiology, Maharana Pratap Dental College, Kanpur, India., Schouman T; Institut de Biomecanique Humaine Georges Charpak, Arts et Metiers Institute of Technology, Paris, France; AP-HP, Hopital Pitie-Salpetriere, Service de Chirurgie Maxillo-Faciale, Medecine Sorbonne Universite, Paris, France., Gajny L; Institut de Biomecanique Humaine Georges Charpak, Arts et Metiers Institute of Technology, Paris, France. |
| المصدر: | Journal of dentistry [J Dent] 2024 Aug; Vol. 147, pp. 105130. Date of Electronic Publication: 2024 Jun 13. |
| نوع المنشور: | Journal Article; Research Support, Non-U.S. Gov't |
| اللغة: | English |
| بيانات الدورية: | Publisher: Elsevier Country of Publication: England NLM ID: 0354422 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1879-176X (Electronic) Linking ISSN: 03005712 NLM ISO Abbreviation: J Dent Subsets: MEDLINE |
| أسماء مطبوعة: | Publication: Kidlington : Elsevier Original Publication: Bristol, Eng., Wright. |
| مواضيع طبية MeSH: | Cone-Beam Computed Tomography*/methods , Deep Learning*, Humans ; Retrospective Studies ; Image Processing, Computer-Assisted/methods ; Mandible/diagnostic imaging ; Mandible/anatomy & histology ; Maxilla/diagnostic imaging ; Maxilla/anatomy & histology ; Tomography, X-Ray Computed/methods ; Tooth/diagnostic imaging ; Tooth/anatomy & histology ; Software ; Imaging, Three-Dimensional/methods |
| مستخلص: | Objectives: Segmentation of anatomical structures on dento-maxillo-facial (DMF) computed tomography (CT) or cone beam computed tomography (CBCT) scans is increasingly needed in digital dentistry. The main aim of this research was to propose and evaluate a novel open source tool called DentalSegmentator for fully automatic segmentation of five anatomical structures on DMF CT and CBCT scans: maxilla/upper skull, mandible, upper teeth, lower teeth, and the mandibular canal. Methods: A retrospective sample of 470 CT and CBCT scans was used as a training/validation set. The performance and generalizability of the tool was evaluated by comparing segmentations provided by experts and automatic segmentations in two hold-out test datasets: an internal dataset of 133 CT and CBCT scans acquired before orthognathic surgery and an external dataset of 123 CBCT scans randomly sampled from routine examinations in 5 institutions. Results: The mean overall results in the internal test dataset (n = 133) were a Dice similarity coefficient (DSC) of 92.2 ± 6.3 % and a normalised surface distance (NSD) of 98.2 ± 2.2 %. The mean overall results on the external test dataset (n = 123) were a DSC of 94.2 ± 7.4 % and a NSD of 98.4 ± 3.6 %. Conclusions: The results obtained from this highly diverse dataset demonstrate that this tool can provide fully automatic and robust multiclass segmentation for DMF CT and CBCT scans. To encourage the clinical deployment of DentalSegmentator, the pre-trained nnU-Net model has been made publicly available along with an extension for the 3D Slicer software. Clinical Significance: DentalSegmentator open source 3D Slicer extension provides a free, robust, and easy-to-use approach to obtaining patient-specific three-dimensional models from CT and CBCT scans. These models serve various purposes in a digital dentistry workflow, such as visualization, treatment planning, intervention, and follow-up. Competing Interests: Declaration of competing interest The authors declare the following financial interests/personal relationships which may be considered as potential competing interests G. Dubois and T. Schouman declared relationships with the following company: Materialise. The other authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article (Copyright © 2024 The Author(s). Published by Elsevier Ltd.. All rights reserved.) |
| فهرسة مساهمة: | Keywords: Artificial intelligence; Computer-assisted radiographic image interpretation; Computer-assisted surgery; Cone-beam computed tomography; Dental informatics; Patient-specific modelling |
| تواريخ الأحداث: | Date Created: 20240615 Date Completed: 20240719 Latest Revision: 20240821 |
| رمز التحديث: | 20240821 |
| DOI: | 10.1016/j.jdent.2024.105130 |
| PMID: | 38878813 |
| قاعدة البيانات: | MEDLINE |
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