Segmentation and 3D-Modelling of Single-Rooted Teeth from CBCT Data: An Automatic Strategy Based on Dental Pulp Segmentation and Surface Deformation

Harrison, Jerome; Chantrel, Steeve; Schmittbuhl, Matthieu; Guise, Jacques A. de

doi:10.1007/978-981-10-9035-6_36

Cited by 6 publications

(7 citation statements)

References 13 publications

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“…The deformable surface model sets a method of tooth segmentation with shape transformations and adjustments. Harrison et al (2019) and Zhang et al (2016) proposed segmentation methods that apply deformable surface models. Zhang et al (2016) , after testing on a collected set of 510 clinical CBCT images, verified that the proposed method has an average processing speed of approximately 3–6 s and a higher volume variation ratio.…”

Section: Resultsmentioning

confidence: 99%

Semi or fully automatic tooth segmentation in CBCT images: a review

Zheng,

Gao,

Zhou

et al. 2024

PeerJ Computer Science

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Cone beam computed tomography (CBCT) is widely employed in modern dentistry, and tooth segmentation constitutes an integral part of the digital workflow based on these imaging data. Previous methodologies rely heavily on manual segmentation and are time-consuming and labor-intensive in clinical practice. Recently, with advancements in computer vision technology, scholars have conducted in-depth research, proposing various fast and accurate tooth segmentation methods. In this review, we review 55 articles in this field and discuss the effectiveness, advantages, and disadvantages of each approach. In addition to simple classification and discussion, this review aims to reveal how tooth segmentation methods can be improved by the application and refinement of existing image segmentation algorithms to solve problems such as irregular morphology and fuzzy boundaries of teeth. It is assumed that with the optimization of these methods, manual operation will be reduced, and greater accuracy and robustness in tooth segmentation will be achieved. Finally, we highlight the challenges that still exist in this field and provide prospects for future directions.

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Section: Resultsmentioning

confidence: 99%

Semi or fully automatic tooth segmentation in CBCT images: a review

Zheng,

Gao,

Zhou

et al. 2024

PeerJ Computer Science

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“…As for the embryologic development of teeth, we used pulps as inner structures, which have, in most cases, the same morphological outline as their corresponding teeth. As described by Harrison et al, this technique uses heuristics derived from knowledge of tooth anatomy where the pulp is used as a pre-personalized model for the tooth shape and thus helps supervise 3D segmentation [12]. The following technical description heavily relies on the proposed methodology from Harrison et al [12].…”

Section: Methodsmentioning

confidence: 99%

“…As described by Harrison et al, this technique uses heuristics derived from knowledge of tooth anatomy where the pulp is used as a pre-personalized model for the tooth shape and thus helps supervise 3D segmentation [12]. The following technical description heavily relies on the proposed methodology from Harrison et al [12]. Figure 1 summarizes the main steps of the following methodology.…”

Section: Methodsmentioning

confidence: 99%

“…These planes are then rotated along the x-axis and y-axis to confirm the correct positioning of said planes. Therefore, candidate planes are described by the following equation, as presented by Harrison et al [12]:…”

Section: Teeth Separation Planesmentioning

confidence: 99%

“…Other similar studies have used this concept [9][10][11]. As outlined in our previous research, limitations of using prior shape model segmentation include requiring large databases to cope with shape variability of the natural dentition [12].…”

Section: Introductionmentioning

confidence: 99%

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Automatic Pulp and Teeth Three-Dimensional Modeling of Single and Multi-Rooted Teeth Based on Cone-Beam Computed Tomography Imaging: A Promising Approach With Clinical and Therapeutic Outcomes

Harris¹,

Harris²,

Harrison³

et al. 2023

Cureus

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BackgroundCone-beam computed tomography (CBCT) imaging offers high-quality three-dimensional (3D) acquisition with great spatial resolution, given by the use of isometric voxels, when compared with conventional computed tomography (CT). The current literature supports a median reduction of 76% (up to 85% reduction) of patients' radiation exposure when imaged by CBCT versus CT. Clinical applications of CBCT imaging can benefit both medical and dental professions. Because these images are digital, the use of algorithms can facilitate the diagnosis of pathologies and the management of patients. There is pertinence to developing rapid and efficient segmentation of teeth from facial volumes acquired with CBCT. MethodologyIn this paper, a segmentation algorithm using heuristics based on pulp and teeth anatomy as a prepersonalized model is proposed for both single and multi-rooted teeth. ResultsA quantitative analysis was performed by comparing the results of the algorithm to a gold standard obtained from manual segmentation using the Dice index, average surface distance (ASD), and Mahalanobis distance (MHD) metrics. Qualitative analysis was also performed between the algorithm and the gold standard of 78 teeth. The Dice index average for all pulp segmentation (n = 78) was 83.82% (SD = 6.54%). ASD for all pulp segmentation (n = 78) was 0.21 mm (SD = 0.34 mm). Pulp segmentation compared with MHD averages was 0.19 mm (SD = 0.21 mm). The results of teeth segmentation metrics were similar to pulp segmentation metrics. For the total teeth (n = 78) included in this study, the Dice index average was 92% (SD = 13.10%), ASD was low at 0.19 mm (SD = 0.15 mm), and MHD was 0.11 mm (SD = 0.09 mm). Despite good quantitative results, the qualitative analysis yielded fair results due to large categories. When compared with existing automatic segmentation methods, our approach enables an effective segmentation for both pulp and teeth. ConclusionsOur proposed algorithm for pulp and teeth segmentation yields results that are comparable to those obtained by the state-of-the-art methods in both quantitative and qualitative analysis, thus offering interesting perspectives in many clinical fields of dentistry.

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