Boundary feature fusion network for tooth image segmentation

• URL: http://arxiv.org/abs/2409.03982v1
• Date: Fri, 6 Sep 2024 02:12:21 GMT
• Title: Boundary feature fusion network for tooth image segmentation
• Authors: Dongping Zhang, Zheng Li, Fangao Zeng, Yutong Wei,
• Abstract summary: This paper introduces an innovative tooth segmentation network that integrates boundary information to address the issue of indistinct boundaries between teeth and adjacent tissues. In the most recent STS Data Challenge, our methodology was rigorously tested and received a commendable overall score of 0.91.
• Score: 7.554733074482215
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Tooth segmentation is a critical technology in the field of medical image segmentation, with applications ranging from orthodontic treatment to human body identification and dental pathology assessment. Despite the development of numerous tooth image segmentation models by researchers, a common shortcoming is the failure to account for the challenges of blurred tooth boundaries. Dental diagnostics require precise delineation of tooth boundaries. This paper introduces an innovative tooth segmentation network that integrates boundary information to address the issue of indistinct boundaries between teeth and adjacent tissues. This network's core is its boundary feature extraction module, which is designed to extract detailed boundary information from high-level features. Concurrently, the feature cross-fusion module merges detailed boundary and global semantic information in a synergistic way, allowing for stepwise layer transfer of feature information. This method results in precise tooth segmentation. In the most recent STS Data Challenge, our methodology was rigorously tested and received a commendable overall score of 0.91. When compared to other existing approaches, this score demonstrates our method's significant superiority in segmenting tooth boundaries.

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)
• Multiclass Segmentation using Teeth Attention Modules for Dental X-ray Images [8.041659727964305]
  We propose a novel teeth segmentation model incorporating an M-Net-like structure with Swin Transformers and TAB. The proposed TAB utilizes a unique attention mechanism that focuses specifically on the complex structures of teeth. The proposed architecture effectively captures local and global contextual information, accurately defining each tooth and its surrounding structures.
  arXiv  Detail & Related papers  (2023-11-07T06:20:34Z)
• A Deep Learning Approach to Teeth Segmentation and Orientation from Panoramic X-rays [1.7366868394060984]
  We present a comprehensive approach to teeth segmentation and orientation from panoramic X-ray images, leveraging deep learning techniques. We build our model based on FUSegNet, a popular model originally developed for wound segmentation. We introduce oriented bounding box (OBB) generation through principal component analysis (PCA) for precise tooth orientation estimation.
  arXiv  Detail & Related papers  (2023-10-26T06:01:25Z)
• A Critical Analysis of the Limitation of Deep Learning based 3D Dental Mesh Segmentation Methods in Segmenting Partial Scans [44.44628400981646]
  Tooth segmentation from intraoral scans is a crucial part of digital dentistry. Many Deep Learning based tooth segmentation algorithms have been developed for this task. In most of the cases, high accuracy has been achieved, although, most of the available tooth segmentation techniques make an implicit restrictive assumption of full jaw model.
  arXiv  Detail & Related papers  (2023-04-29T11:58:23Z)
• Reliable Joint Segmentation of Retinal Edema Lesions in OCT Images [55.83984261827332]
  In this paper, we propose a novel reliable multi-scale wavelet-enhanced transformer network. We develop a novel segmentation backbone that integrates a wavelet-enhanced feature extractor network and a multi-scale transformer module. Our proposed method achieves better segmentation accuracy with a high degree of reliability as compared to other state-of-the-art segmentation approaches.
  arXiv  Detail & Related papers  (2022-12-01T07:32:56Z)
• Semantic decomposition Network with Contrastive and Structural Constraints for Dental Plaque Segmentation [33.40662847763453]
  Dental plaque segmentation is a challenging task that requires identifying teeth and dental plaque subjected to semanticblur regions. We propose a semantic decomposition network (SDNet) that introduces two single-task branches to address the segmentation of teeth and dental plaque.
  arXiv  Detail & Related papers  (2022-08-12T14:10:29Z)
• Simultaneous Bone and Shadow Segmentation Network using Task Correspondence Consistency [60.378180265885945]
  We propose a single end-to-end network with a shared transformer-based encoder and task independent decoders for simultaneous bone and shadow segmentation. We also introduce a correspondence consistency loss which makes sure that network utilizes the inter-dependency between the bone surface and its corresponding shadow to refine the segmentation.
  arXiv  Detail & Related papers  (2022-06-16T22:37:05Z)
• 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)
• 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)
• An Uncertainty-Driven GCN Refinement Strategy for Organ Segmentation [53.425900196763756]
  We propose a segmentation refinement method based on uncertainty analysis and graph convolutional networks. We employ the uncertainty levels of the convolutional network in a particular input volume to formulate a semi-supervised graph learning problem. We show that our method outperforms the state-of-the-art CRF refinement method by improving the dice score by 1% for the pancreas and 2% for spleen.
  arXiv  Detail & Related papers  (2020-12-06T18:55:07Z)

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.