Anatomy-Guided Parallel Bottleneck Transformer Network for Automated Evaluation of Root Canal Therapy

• URL: http://arxiv.org/abs/2105.00381v1
• Date: Sun, 2 May 2021 02:38:31 GMT
• Title: Anatomy-Guided Parallel Bottleneck Transformer Network for Automated Evaluation of Root Canal Therapy
• Authors: Yunxiang Li, Guodong Zeng, Yifan Zhang, Jun Wang, Qianni Zhang, Qun Jin, Lingling Sun, Qisi Lian, Neng Xia, Ruizi Peng, Kai Tang, Yaqi Wang, Shuai Wang
• Abstract summary: The root canal filling result in X-ray image is a significant step for the root canal therapy. For obtaining accurate anatomy-guided features, a curve fitting segmentation is proposed to segment the fuzzy boundary. And a Parallel Bottleneck Transformer network (PBT-Net) is introduced as the classification network for the final evaluation.
• Score: 13.768248182867673
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Objective: Accurate evaluation of the root canal filling result in X-ray image is a significant step for the root canal therapy, which is based on the relative position between the apical area boundary of tooth root and the top of filled gutta-percha in root canal as well as the shape of the tooth root and so on to classify the result as correct-filling, under-filling or over-filling. Methods: We propose a novel anatomy-guided Transformer diagnosis network. For obtaining accurate anatomy-guided features, a polynomial curve fitting segmentation is proposed to segment the fuzzy boundary. And a Parallel Bottleneck Transformer network (PBT-Net) is introduced as the classification network for the final evaluation. Results, and conclusion: Our numerical experiments show that our anatomy-guided PBT-Net improves the accuracy from 40\% to 85\% relative to the baseline classification network. Comparing with the SOTA segmentation network indicates that the ASD is significantly reduced by 30.3\% through our fitting segmentation. Significance: Polynomial curve fitting segmentation has a great segmentation effect for extremely fuzzy boundaries. The prior knowledge guided classification network is suitable for the evaluation of root canal therapy greatly. And the new proposed Parallel Bottleneck Transformer for realizing self-attention is general in design, facilitating a broad use in most backbone networks.

Related papers

• RSF-Conv: Rotation-and-Scale Equivariant Fourier Parameterized Convolution for Retinal Vessel Segmentation [58.618797429661754]
  We propose a rotation-and-scale equivariant Fourier parameterized convolution (RSF-Conv) specifically for retinal vessel segmentation. As a general module, RSF-Conv can be integrated into existing networks in a plug-and-play manner. To demonstrate the effectiveness of RSF-Conv, we also apply RSF-Conv+U-Net and RSF-Conv+Iter-Net to retinal artery/vein classification.
  arXiv  Detail & Related papers  (2023-09-27T13:14:57Z)
• 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)
• 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)
• Hepatic vessel segmentation based on 3Dswin-transformer with inductive biased multi-head self-attention [46.46365941681487]
  We propose a robust end-to-end vessel segmentation network called Indu BIased Multi-Head Attention Vessel Net. We introduce the voxel-wise embedding rather than patch-wise embedding to locate precise liver vessel voxels. On the other hand, we propose inductive biased multi-head self-attention which learns inductive biased relative positional embedding from absolute position embedding.
  arXiv  Detail & Related papers  (2021-11-05T10:17:08Z)
• GT U-Net: A U-Net Like Group Transformer Network for Tooth Root Segmentation [11.177365038111438]
  We propose a novel end-to-end U-Net like Group Transformer Network (GT U-Net) for the tooth root segmentation. The proposed network retains the essential structure of U-Net but each of the encoders and decoders is replaced by a group Transformer. Our proposed network achieves the state-of-the-art performance on our collected tooth root segmentation dataset and the public retina dataset.
  arXiv  Detail & Related papers  (2021-09-30T02:39:07Z)
• 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)
• Multi-Task Neural Networks with Spatial Activation for Retinal Vessel Segmentation and Artery/Vein Classification [49.64863177155927]
  We propose a multi-task deep neural network with spatial activation mechanism to segment full retinal vessel, artery and vein simultaneously. The proposed network achieves pixel-wise accuracy of 95.70% for vessel segmentation, and A/V classification accuracy of 94.50%, which is the state-of-the-art performance for both tasks.
  arXiv  Detail & Related papers  (2020-07-18T05:46:47Z)
• Adaptive feature recombination and recalibration for semantic segmentation with Fully Convolutional Networks [57.64866581615309]
  We propose recombination of features and a spatially adaptive recalibration block that is adapted for semantic segmentation with Fully Convolutional Networks. Results indicate that Recombination and Recalibration improve the results of a competitive baseline, and generalize across three different problems.
  arXiv  Detail & Related papers  (2020-06-19T15:45:03Z)
• PraNet: Parallel Reverse Attention Network for Polyp Segmentation [155.93344756264824]
  We propose a parallel reverse attention network (PraNet) for accurate polyp segmentation in colonoscopy images. We first aggregate the features in high-level layers using a parallel partial decoder (PPD) In addition, we mine the boundary cues using a reverse attention (RA) module, which is able to establish the relationship between areas and boundary cues.
  arXiv  Detail & Related papers  (2020-06-13T08:13:43Z)
• Convolutional Neural Networks based automated segmentation and labelling of the lumbar spine X-ray [0.0]
  The aim of this study is to investigate the segmentation accuracies of different segmentation networks trained on 730 manually annotated lateral lumbar spine X-rays. Instance segmentation networks were compared to semantic segmentation networks.
  arXiv  Detail & Related papers  (2020-04-04T20:15:03Z)

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.