TFormer: 3D Tooth Segmentation in Mesh Scans with Geometry Guided Transformer

• URL: http://arxiv.org/abs/2210.16627v1
• Date: Sat, 29 Oct 2022 15:20:54 GMT
• Title: TFormer: 3D Tooth Segmentation in Mesh Scans with Geometry Guided Transformer
• Authors: Huimin Xiong, Kunle Li, Kaiyuan Tan, Yang Feng, Joey Tianyi Zhou, Jin Hao, Zuozhu Liu
• Abstract summary: Optical Intra-oral Scanners (IOS) are widely used in digital dentistry, providing 3-Dimensional (3D) and high-resolution geometrical information of dental crowns and the gingiva. Previous methods are error-prone in complicated tooth-tooth or tooth-gingiva boundaries, and usually exhibit unsatisfactory results across various patients. We propose a novel method based on 3D transformer architectures that is evaluated with large-scale and high-resolution 3D IOS datasets.
• Score: 37.47317212620463
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Optical Intra-oral Scanners (IOS) are widely used in digital dentistry, providing 3-Dimensional (3D) and high-resolution geometrical information of dental crowns and the gingiva. Accurate 3D tooth segmentation, which aims to precisely delineate the tooth and gingiva instances in IOS, plays a critical role in a variety of dental applications. However, segmentation performance of previous methods are error-prone in complicated tooth-tooth or tooth-gingiva boundaries, and usually exhibit unsatisfactory results across various patients, yet the clinically applicability is not verified with large-scale dataset. In this paper, we propose a novel method based on 3D transformer architectures that is evaluated with large-scale and high-resolution 3D IOS datasets. Our method, termed TFormer, captures both local and global dependencies among different teeth to distinguish various types of teeth with divergent anatomical structures and confusing boundaries. Moreover, we design a geometry guided loss based on a novel point curvature to exploit boundary geometric features, which helps refine the boundary predictions for more accurate and smooth segmentation. We further employ a multi-task learning scheme, where an additional teeth-gingiva segmentation head is introduced to improve the performance. Extensive experimental results in a large-scale dataset with 16,000 IOS, the largest IOS dataset to our best knowledge, demonstrate that our TFormer can surpass existing state-of-the-art baselines with a large margin, with its utility in real-world scenarios verified by a clinical applicability test.

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)
• TSegFormer: 3D Tooth Segmentation in Intraoral Scans with Geometry Guided Transformer [47.18526074157094]
  Optical Intraoral Scanners (IOSs) are widely used in digital dentistry to provide detailed 3D information of dental crowns and the gingiva. Previous methods are error-prone at complicated boundaries and exhibit unsatisfactory results across patients. We propose TSegFormer which captures both local and global dependencies among different teeth and the gingiva in the IOS point clouds with a multi-task 3D transformer architecture.
  arXiv  Detail & Related papers  (2023-11-22T08:45:01Z)
• 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)
• 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)
• A fully automated method for 3D individual tooth identification and segmentation in dental CBCT [1.567576360103422]
  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.
  arXiv  Detail & Related papers  (2021-02-11T15:07:23Z)
• 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)
• Volumetric Medical Image Segmentation: A 3D Deep Coarse-to-fine Framework and Its Adversarial Examples [74.92488215859991]
  We propose a novel 3D-based coarse-to-fine framework to efficiently tackle these challenges. The proposed 3D-based framework outperforms their 2D counterparts by a large margin since it can leverage the rich spatial information along all three axes. We conduct experiments on three datasets, the NIH pancreas dataset, the JHMI pancreas dataset and the JHMI pathological cyst dataset.
  arXiv  Detail & Related papers  (2020-10-29T15:39:19Z)

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.