High-Resolution Segmentation of Tooth Root Fuzzy Edge Based on Polynomial Curve Fitting with Landmark Detection

• URL: http://arxiv.org/abs/2103.04258v1
• Date: Sun, 7 Mar 2021 04:28:09 GMT
• Title: High-Resolution Segmentation of Tooth Root Fuzzy Edge Based on Polynomial Curve Fitting with Landmark Detection
• Authors: Yunxiang Li, Yifan Zhang, Yaqi Wang, Shuai Wang, Ruizi Peng, Kai Tang, Qianni Zhang, Jun Wang, Qun Jin, Lingling Sun
• Abstract summary: We propose a model for high-resolution segmentation based on curve fitting with landmark detection (HS-PCL) It is based on detecting multiple landmarks evenly distributed on the edge of the tooth root to fit a smooth curve as the segmentation of the tooth root. In our model, a maximum number of the shortest algorithm (MNSDA) is proposed to automatically reduce the negative influence of the wrong landmarks.
• Score: 14.733417048938518
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: As the most economical and routine auxiliary examination in the diagnosis of root canal treatment, oral X-ray has been widely used by stomatologists. It is still challenging to segment the tooth root with a blurry boundary for the traditional image segmentation method. To this end, we propose a model for high-resolution segmentation based on polynomial curve fitting with landmark detection (HS-PCL). It is based on detecting multiple landmarks evenly distributed on the edge of the tooth root to fit a smooth polynomial curve as the segmentation of the tooth root, thereby solving the problem of fuzzy edge. In our model, a maximum number of the shortest distances algorithm (MNSDA) is proposed to automatically reduce the negative influence of the wrong landmarks which are detected incorrectly and deviate from the tooth root on the fitting result. Our numerical experiments demonstrate that the proposed approach not only reduces Hausdorff95 (HD95) by 33.9% and Average Surface Distance (ASD) by 42.1% compared with the state-of-the-art method, but it also achieves excellent results on the minute quantity of datasets, which greatly improves the feasibility of automatic root canal therapy evaluation by medical image computing.

Related papers

• 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)
• B-Spine: Learning B-Spline Curve Representation for Robust and Interpretable Spinal Curvature Estimation [50.208310028625284]
  We propose B-Spine, a novel deep learning pipeline to learn B-spline curve representation of the spine. We estimate the Cobb angles for spinal curvature estimation from low-quality X-ray images.
  arXiv  Detail & Related papers  (2023-10-14T15:34:57Z)
• Teeth And Root Canals Segmentation Using ZXYFormer With Uncertainty Guidance And Weight Transfer [8.497690081160087]
  This study attempts to segment teeth and root-canals simultaneously from CBCT images. Teeth and root canals are very different in morphology, and it is difficult for a simple network to identify them precisely. We propose a coarse-to-fine segmentation method based on inverse feature fusion transformer and uncertainty estimation.
  arXiv  Detail & Related papers  (2023-08-14T11:06:28Z)
• Enhanced Sharp-GAN For Histopathology Image Synthesis [63.845552349914186]
  Histopathology image synthesis aims to address the data shortage issue in training deep learning approaches for accurate cancer detection. We propose a novel approach that enhances the quality of synthetic images by using nuclei topology and contour regularization. The proposed approach outperforms Sharp-GAN in all four image quality metrics on two datasets.
  arXiv  Detail & Related papers  (2023-01-24T17:54:01Z)
• 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)
• Orientation-guided Graph Convolutional Network for Bone Surface Segmentation [51.51690515362261]
  We propose an orientation-guided graph convolutional network to improve connectivity while segmenting the bone surface. Our approach improves over the state-of-the-art methods by 5.01% in connectivity metric.
  arXiv  Detail & Related papers  (2022-06-16T23:01:29Z)
• 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)
• Tooth Instance Segmentation from Cone-Beam CT Images through Point-based Detection and Gaussian Disentanglement [5.937871999460492]
  We propose a point-based tooth localization network that disentangles each individual tooth based on a Gaussian disentanglement objective function. Experimental results demonstrate that the proposed algorithm outperforms state-of-the-art approaches by increasing the average precision of detection by 9.1%.
  arXiv  Detail & Related papers  (2021-02-02T05:15:50Z)
• 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)
• An Adaptive Enhancement Based Hybrid CNN Model for Digital Dental X-ray Positions Classification [1.0672152844970149]
  A novel solution based on adaptive histogram equalization and convolution neural network (CNN) is proposed. The accuracy and specificity of the test set exceeded 90%, and the AUC reached 0.97.
  arXiv  Detail & Related papers  (2020-05-01T13:55:44Z)
• Individual Tooth Detection and Identification from Dental Panoramic X-Ray Images via Point-wise Localization and Distance Regularization [10.877276642014515]
  The proposed network initially performs center point regression for all the anatomical teeth, which automatically identifies each tooth. Teeth boxes are individually localized using a cascaded neural network on a patch basis. The experimental results demonstrate that the proposed algorithm outperforms state-of-the-art approaches.
  arXiv  Detail & Related papers  (2020-04-12T04:14:14Z)

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.