Deep convolutional neural network-based automated segmentation of the maxillofacial complex from cone-beam computed tomography:A validation study

Preda, Flavia; Morgan, Nermin; Gerven, Adriaan Van; Reis, Fernanda Nogueira; Smolders, A.; Wang, Xiaotong; Nomidis, Stefanos K.; Shaheen, Eman; Willems, Holger; Jacobs, Reinhilde

doi:10.1016/j.jdent.2022.104238

Cited by 48 publications

(20 citation statements)

References 32 publications

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“…Nevertheless, previous studies developed CNN models for performing multi-class automated segmentation of the maxillofacial complex on CT (Dot et al, 2022) and CBCT images (Ham et al, 2018;Preda et al, 2022;Wang et al, 2021). These studies reported an acceptable performance of the CNN models with the DSC values ranging from 82.6% to 96.8%.…”

Section: Discussionmentioning

confidence: 99%

“…As this is the first study which applied an AI‐driven tool for the 3D modeling of maxillary alveolar bone on CBCT images, thereby, comparison with already existing studies was deemed difficult. Nevertheless, previous studies developed CNN models for performing multi‐class automated segmentation of the maxillofacial complex on CT (Dot et al, 2022) and CBCT images (Ham et al, 2018; Preda et al, 2022; Wang et al, 2021). These studies reported an acceptable performance of the CNN models with the DSC values ranging from 82.6% to 96.8%.…”

Section: Discussionmentioning

confidence: 99%

“…As the CNN architectures normally do not accept large-sized images in input due to the computation and memory limitations, such as large FOV CBCT scans, a two-step method was employed, as previously described by Preda et al (2022). In the first step, the U-Net model produced a rough segmentation of the region of interest.…”

Section: Cnns Model Architecturementioning

confidence: 99%

“…Recently, artificial intelligence (AI)‐driven convolutional neural networks (CNNs) have been developed for overcoming the limitations associated with the conventional segmentation approaches. Previous studies have reported the application of CNN‐based models to automatically and accurately segment teeth (Fontenele et al, 2022; Shaheen et al, 2021), dentomaxillofacial complex (Preda et al, 2022; Wang et al, 2021), mandible (Verhelst et al, 2021), pharyngeal airway space (Shujaat, Jazil, et al, 2021), and mandibular canal (Lahoud et al, 2022) on CBCT images. However, CNN‐based automated alveolar bone segmentation with a detailed outlining of the alveolar crest on CBCT images has not yet been investigated.…”

Section: Introductionmentioning

confidence: 99%

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Convolutional neural network‐based automated maxillary alveolar bone segmentation on cone‐beam computed tomography images

Fontenele

Gerhardt

Pícoli

et al. 2023

Clinical Oral Implants Res

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Objectives To develop and assess the performance of a novel artificial intelligence (AI)‐driven convolutional neural network (CNN)‐based tool for automated three‐dimensional (3D) maxillary alveolar bone segmentation on cone‐beam computed tomography (CBCT) images. Materials and Methods A total of 141 CBCT scans were collected for performing training (n = 99), validation (n = 12), and testing (n = 30) of the CNN model for automated segmentation of the maxillary alveolar bone and its crestal contour. Following automated segmentation, the 3D models with under‐ or overestimated segmentations were refined by an expert for generating a refined‐AI (R‐AI) segmentation. The overall performance of CNN model was assessed. Also, 30% of the testing sample was randomly selected and manually segmented to compare the accuracy of AI and manual segmentation. Additionally, the time required to generate a 3D model was recorded in seconds (s). Results The accuracy metrics of automated segmentation showed an excellent range of values for all accuracy metrics. However, the manual method (95% HD: 0.20 ± 0.05 mm; IoU: 95% ± 3.0; DSC: 97% ± 2.0) showed slightly better performance than the AI segmentation (95% HD: 0.27 ± 0.03 mm; IoU: 92% ± 1.0; DSC: 96% ± 1.0). There was a statistically significant difference of the time‐consumed among the segmentation methods (p < .001). The AI‐driven segmentation (51.5 ± 10.9 s) was 116 times faster than the manual segmentation (5973.3 ± 623.6 s). The R‐AI method showed intermediate time‐consumed (1666.7 ± 588.5 s). Conclusion Although the manual segmentation showed slightly better performance, the novel CNN‐based tool also provided a highly accurate segmentation of the maxillary alveolar bone and its crestal contour consuming 116 times less than the manual approach.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Cnns Model Architecturementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Convolutional neural network‐based automated maxillary alveolar bone segmentation on cone‐beam computed tomography images

Fontenele

Gerhardt

Pícoli

et al. 2023

Clinical Oral Implants Res

Self Cite

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“…To highlight various tissue properties of the BT, different complementary modalities like T1‐weighted (T1), T2‐weighted (T2), and so forth, based on 3D MRI are acquired 10–12 . The manual segmentation process is difficult and consumes more time 13,14 . Thus that, automatic BTS enhances monitoring, treatment, and BT analysis, and it is beneficial.…”

Section: Introductionmentioning

confidence: 99%

Brain tumor segmentation and survival time prediction using graph momentum fully convolutional network with modified Elman spike neural network

Ramkumar,

Kumar,

Padmapriya

et al. 2024

Int J Imaging Syst Tech

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Brain tumor segmentation (BTS) from magnetic resonance imaging (MRI) scans is crucial for the diagnosis, treatment planning, and monitoring of therapeutic results. Thus, this research work proposes a novel graph momentum fully convolutional network with a modified Elman spike neural network (MESNN) for BTS and overall survival prediction (OSP). Initially, the introduced graph momentum fully convolutional network segments the brain tumor as enhanced tumor, the tumor core, and the whole tumor from the pre‐processed MRI scans. Second, the texture, intensity, shape, and wavelet features were extracted from the segmented tumors. Then, the horse herd optimization algorithm is utilized to minimize the feature's dimensionality. Finally, the OSP is performed by the MESNN which classifies the survival prediction of a patient as long‐term, mid‐term, and short‐term. The achieved segmentation accuracy of proposed method is 97% and the survival prediction's average RMSE is 215.5.

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Mapping the Use of Artificial Intelligence–Based Image Analysis for Clinical Decision‐Making in Dentistry: A Scoping Review

Chen,

Dhawan,

Liu

et al. 2024

Clinical & Exp Dental Res

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ObjectivesArtificial intelligence (AI) is an emerging field in dentistry. AI is gradually being integrated into dentistry to improve clinical dental practice. The aims of this scoping review were to investigate the application of AI in image analysis for decision‐making in clinical dentistry and identify trends and research gaps in the current literature.Material and MethodsThis review followed the guidelines provided by the Preferred Reporting Items for Systematic Reviews and Meta‐Analyses Extension for Scoping Reviews (PRISMA‐ScR). An electronic literature search was performed through PubMed and Scopus. After removing duplicates, a preliminary screening based on titles and abstracts was performed. A full‐text review and analysis were performed according to predefined inclusion criteria, and data were extracted from eligible articles.ResultsOf the 1334 articles returned, 276 met the inclusion criteria (consisting of 601,122 images in total) and were included in the qualitative synthesis. Most of the included studies utilized convolutional neural networks (CNNs) on dental radiographs such as orthopantomograms (OPGs) and intraoral radiographs (bitewings and periapicals). AI was applied across all fields of dentistry ‐ particularly oral medicine, oral surgery, and orthodontics ‐ for direct clinical inference and segmentation. AI‐based image analysis was use in several components of the clinical decision‐making process, including diagnosis, detection or classification, prediction, and management.ConclusionsA variety of machine learning and deep learning techniques are being used for dental image analysis to assist clinicians in making accurate diagnoses and choosing appropriate interventions in a timely manner.

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Deep convolutional neural network-based automated segmentation of the maxillofacial complex from cone-beam computed tomography:A validation study

Cited by 48 publications

References 32 publications

Convolutional neural network‐based automated maxillary alveolar bone segmentation on cone‐beam computed tomography images

Convolutional neural network‐based automated maxillary alveolar bone segmentation on cone‐beam computed tomography images

Brain tumor segmentation and survival time prediction using graph momentum fully convolutional network with modified Elman spike neural network

Mapping the Use of Artificial Intelligence–Based Image Analysis for Clinical Decision‐Making in Dentistry: A Scoping Review

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