FDNet: Feature Decoupled Segmentation Network for Tooth CBCT Image

• URL: http://arxiv.org/abs/2311.06551v2
• Date: Thu, 5 Sep 2024 14:47:24 GMT
• Title: FDNet: Feature Decoupled Segmentation Network for Tooth CBCT Image
• Authors: Xiang Feng, Chengkai Wang, Chengyu Wu, Yunxiang Li, Yongbo He, Shuai Wang, Yaiqi Wang,
• Abstract summary: We propose FDNet, a Feature Decoupled Network, to excel in the face of the variable dental conditions encountered in CBCT scans. The framework's effectiveness is validated through rigorous benchmarks, achieving the top Dice and IoU scores of 85.28% and 75.23%, respectively.
• Score: 5.455286826028825
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Precise Tooth Cone Beam Computed Tomography (CBCT) image segmentation is crucial for orthodontic treatment planning. In this paper, we propose FDNet, a Feature Decoupled Segmentation Network, to excel in the face of the variable dental conditions encountered in CBCT scans, such as complex artifacts and indistinct tooth boundaries. The Low-Frequency Wavelet Transform (LF-Wavelet) is employed to enrich the semantic content by emphasizing the global structural integrity of the teeth, while the SAM encoder is leveraged to refine the boundary delineation, thus improving the contrast between adjacent dental structures. By integrating these dual aspects, FDNet adeptly addresses the semantic gap, providing a detailed and accurate segmentation. The framework's effectiveness is validated through rigorous benchmarks, achieving the top Dice and IoU scores of 85.28% and 75.23%, respectively. This innovative decoupling of semantic and boundary features capitalizes on the unique strengths of each element to elevate the quality of segmentation performance.

Related papers

• MDFI-Net: Multiscale Differential Feature Interaction Network for Accurate Retinal Vessel Segmentation [3.152646316470194]
  This paper proposes a feature-enhanced interaction network based on DPCN, named MDFI-Net. The proposed MDFI-Net achieves segmentation performance superior to state-of-the-art methods on public datasets.
  arXiv  Detail & Related papers  (2024-10-20T16:42:22Z)
• Sparse Anatomical Prompt Semi-Supervised Learning with Masked Image Modeling for CBCT Tooth Segmentation [10.617296334463942]
  tooth identification and segmentation in Cone Beam Computed Tomography (CBCT) dental images can significantly enhance the efficiency and precision of manual diagnoses performed by dentists. Existing segmentation methods are mainly developed based on large data volumes training, on which their annotations are extremely time-consuming. This study proposes a tasked-oriented Masked Auto-Encoder paradigm to effectively utilize large amounts of unlabeled data to achieve accurate tooth segmentation with limited labeled data.
  arXiv  Detail & Related papers  (2024-02-07T05:05:21Z)
• 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)
• 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)
• 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)
• InDuDoNet+: A Model-Driven Interpretable Dual Domain Network for Metal Artifact Reduction in CT Images [53.4351366246531]
  We construct a novel interpretable dual domain network, termed InDuDoNet+, into which CT imaging process is finely embedded. We analyze the CT values among different tissues, and merge the prior observations into a prior network for our InDuDoNet+, which significantly improve its generalization performance.
  arXiv  Detail & Related papers  (2021-12-23T15:52:37Z)
• 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)
• Batch Coherence-Driven Network for Part-aware Person Re-Identification [79.33809815035127]
  Existing part-aware person re-identification methods typically employ two separate steps: namely, body part detection and part-level feature extraction. We propose NetworkBCDNet that bypasses body part during both the training and testing phases while still semantically aligned features.
  arXiv  Detail & Related papers  (2020-09-21T09:04:13Z)
• Pose-Aware Instance Segmentation Framework from Cone Beam CT Images for Tooth Segmentation [9.880428545498662]
  Individual tooth segmentation from cone beam computed tomography (CBCT) images is essential for an anatomical understanding of orthodontic structures. The presence of severe metal artifacts in CBCT images hinders the accurate segmentation of each individual tooth. We propose a neural network for pixel-wise labeling to exploit an instance segmentation framework that is robust to metal artifacts.
  arXiv  Detail & Related papers  (2020-02-06T07:57:34Z)

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.