A fully automated method for 3D individual tooth identification and segmentation in dental CBCT

• URL: http://arxiv.org/abs/2102.06060v1
• Date: Thu, 11 Feb 2021 15:07:23 GMT
• Title: A fully automated method for 3D individual tooth identification and segmentation in dental CBCT
• Authors: Tae Jun Jang, Kang Cheol Kim, Hyun Cheol Cho, Jin Keun Seo
• Abstract summary: This paper proposes a fully automated method of identifying and segmenting 3D individual teeth from dental CBCT images. The proposed method addresses the aforementioned difficulty by developing a deep learning-based hierarchical multi-step model. Experimental results showed that the proposed method achieved an F1-score of 93.35% for tooth identification and a Dice similarity coefficient of 94.79% for individual 3D tooth segmentation.
• Score: 1.567576360103422
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Accurate and automatic segmentation of three-dimensional (3D) individual teeth from cone-beam computerized tomography (CBCT) images is a challenging problem because of the difficulty in separating an individual tooth from adjacent teeth and its surrounding alveolar bone. Thus, this paper proposes a fully automated method of identifying and segmenting 3D individual teeth from dental CBCT images. The proposed method addresses the aforementioned difficulty by developing a deep learning-based hierarchical multi-step model. First, it automatically generates upper and lower jaws panoramic images to overcome the computational complexity caused by high-dimensional data and the curse of dimensionality associated with limited training dataset. The obtained 2D panoramic images are then used to identify 2D individual teeth and capture loose- and tight- regions of interest (ROIs) of 3D individual teeth. Finally, accurate 3D individual tooth segmentation is achieved using both loose and tight ROIs. Experimental results showed that the proposed method achieved an F1-score of 93.35% for tooth identification and a Dice similarity coefficient of 94.79% for individual 3D tooth segmentation. The results demonstrate that the proposed method provides an effective clinical and practical framework for digital dentistry.

Related papers

• A Multi-Stage Framework for 3D Individual Tooth Segmentation in Dental CBCT [7.6057981800052845]
  Cone beam computed tomography (CBCT) is a common way of diagnosing dental diseases. Deep learning based methods have achieved convincing results in medical image processing. We propose a multi-stage framework for 3D tooth related generalization in dental CBCT.
  arXiv  Detail & Related papers  (2024-07-15T04:23:28Z)
• An efficient method to automate tooth identification and 3D bounding box extraction from Cone Beam CT Images [33.7054351451505]
  This paper proposes a method for automatically detecting, identifying, and extracting teeth from CBCT images. Teeth are pinpointed and labeled using a single-stage object detector. Broom boxes are delineated and identified to create three-dimensional representations of each tooth.
  arXiv  Detail & Related papers  (2024-07-08T12:59:28Z)
• ID-to-3D: Expressive ID-guided 3D Heads via Score Distillation Sampling [96.87575334960258]
  ID-to-3D is a method to generate identity- and text-guided 3D human heads with disentangled expressions. Results achieve an unprecedented level of identity-consistent and high-quality texture and geometry generation.
  arXiv  Detail & Related papers  (2024-05-26T13:36:45Z)
• 3D Structure-guided Network for Tooth Alignment in 2D Photograph [51.9046939030457]
  A 2D photograph depicting aligned teeth prior to orthodontic treatment is crucial for effective dentist-patient communication. We propose a 3D structure-guided tooth alignment network that takes 2D photographs as input and aligns the teeth within the 2D image space. We evaluate our network on various facial photographs, demonstrating its exceptional performance and strong applicability within the orthodontic industry.
  arXiv  Detail & Related papers  (2023-10-17T09:44:30Z)
• An Implicit Parametric Morphable Dental Model [79.29420177904022]
  We present the first parametric 3D morphable dental model for both teeth and gum. It is based on a component-wise representation for each tooth and the gum, together with a learnable latent code for each of such components. Our reconstruction quality is on par with the most advanced global implicit representations while enabling novel applications.
  arXiv  Detail & Related papers  (2022-11-21T12:23:54Z)
• Segmentation of 3D Dental Images Using Deep Learning [0.0]
  3D image segmentation is a recent and crucial step in many medical analysis and recognition schemes. This paper provides a multi-phase Deep Learning-based system that hybridizes various efficient methods in order to get the best 3D segmentation output.
  arXiv  Detail & Related papers  (2022-07-19T23:17:54Z)
• Two-Stream Graph Convolutional Network for Intra-oral Scanner Image Segmentation [133.02190910009384]
  We propose a two-stream graph convolutional network (i.e., TSGCN) to handle inter-view confusion between different raw attributes. Our TSGCN significantly outperforms state-of-the-art methods in 3D tooth (surface) segmentation.
  arXiv  Detail & Related papers  (2022-04-19T10:41:09Z)
• Developing a Novel Approach for Periapical Dental Radiographs Segmentation [1.332560004325655]
  The proposed algorithm is made of two stages. The first stage is pre-processing. The second and main part of this algorithm calculated rotation degree and uses the integral projection method for tooth isolation. Experimental results show that this algorithm is robust and achieves high accuracy.
  arXiv  Detail & Related papers  (2021-11-13T17:25:35Z)
• Dense Representative Tooth Landmark/axis Detection Network on 3D Model [32.81858923141152]
  We propose a deep learning approach with a labeled dataset by professional dentists to the tooth landmark/axis detection on tooth model. Our method can extract not only tooth landmarks in the form of point (e.g. cusps) but also axes that measure the tooth angulation and inclination. The proposed network takes as input a 3D tooth model and predicts various types of the tooth landmarks and axes.
  arXiv  Detail & Related papers  (2021-11-08T00:42:22Z)
• Two-Stage Mesh Deep Learning for Automated Tooth Segmentation and Landmark Localization on 3D Intraoral Scans [56.55092443401416]
  emphiMeshSegNet in the first stage of TS-MDL reached an averaged Dice similarity coefficient (DSC) at 0.953pm0.076$, significantly outperforming the original MeshSegNet. PointNet-Reg achieved a mean absolute error (MAE) of $0.623pm0.718, mm$ in distances between the prediction and ground truth for $44$ landmarks, which is superior compared with other networks for landmark detection.
  arXiv  Detail & Related papers  (2021-09-24T13:00:26Z)
• TSGCNet: Discriminative Geometric Feature Learning with Two-Stream GraphConvolutional Network for 3D Dental Model Segmentation [141.2690520327948]
  We propose a two-stream graph convolutional network (TSGCNet) to learn multi-view information from different geometric attributes. We evaluate our proposed TSGCNet on a real-patient dataset of dental models acquired by 3D intraoral scanners.
  arXiv  Detail & Related papers  (2020-12-26T08:02:56Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.