Processing and Segmentation of Human Teeth from 2D Images using Weakly
Supervised Learning
- URL: http://arxiv.org/abs/2311.07398v2
- Date: Mon, 26 Feb 2024 09:54:34 GMT
- Title: Processing and Segmentation of Human Teeth from 2D Images using Weakly
Supervised Learning
- Authors: Tom\'a\v{s} Kunzo, Viktor Kocur, Luk\'a\v{s} Gajdo\v{s}ech, Martin
Madaras
- Abstract summary: We propose a weakly supervised approach for teeth segmentation that reduces the need for manual annotation.
Our method utilizes the output heatmaps and intermediate feature maps from a keypoint detection network to guide the segmentation process.
Our method offers a cost-effective and efficient solution for teeth segmentation in real-world dental applications.
- Score: 1.6385815610837167
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Teeth segmentation is an essential task in dental image analysis for accurate
diagnosis and treatment planning. While supervised deep learning methods can be
utilized for teeth segmentation, they often require extensive manual annotation
of segmentation masks, which is time-consuming and costly. In this research, we
propose a weakly supervised approach for teeth segmentation that reduces the
need for manual annotation. Our method utilizes the output heatmaps and
intermediate feature maps from a keypoint detection network to guide the
segmentation process. We introduce the TriDental dataset, consisting of 3000
oral cavity images annotated with teeth keypoints, to train a teeth keypoint
detection network. We combine feature maps from different layers of the
keypoint detection network, enabling accurate teeth segmentation without
explicit segmentation annotations. The detected keypoints are also used for
further refinement of the segmentation masks. Experimental results on the
TriDental dataset demonstrate the superiority of our approach in terms of
accuracy and robustness compared to state-of-the-art segmentation methods. Our
method offers a cost-effective and efficient solution for teeth segmentation in
real-world dental applications, eliminating the need for extensive manual
annotation efforts.
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) - Sparse Anatomical Prompt Semi-Supervised Learning with Masked Image Modeling for CBCT Tooth Segmentation [9.373643627609336]
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) - 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) - Self-Supervised Correction Learning for Semi-Supervised Biomedical Image
Segmentation [84.58210297703714]
We propose a self-supervised correction learning paradigm for semi-supervised biomedical image segmentation.
We design a dual-task network, including a shared encoder and two independent decoders for segmentation and lesion region inpainting.
Experiments on three medical image segmentation datasets for different tasks demonstrate the outstanding performance of our method.
arXiv Detail & Related papers (2023-01-12T08:19:46Z) - CTooth: A Fully Annotated 3D Dataset and Benchmark for Tooth Volume
Segmentation on Cone Beam Computed Tomography Images [19.79983193894742]
3D tooth segmentation is a prerequisite for computer-aided dental diagnosis and treatment.
Deep learning-based segmentation methods produce convincing results, but it requires a large quantity of ground truth for training.
In this paper, we establish a fully annotated cone beam computed tomography dataset CTooth with tooth gold standard.
arXiv Detail & Related papers (2022-06-17T13:48:35Z) - 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) - 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) - 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) - Weakly-supervised Learning For Catheter Segmentation in 3D Frustum
Ultrasound [74.22397862400177]
We propose a novel Frustum ultrasound based catheter segmentation method.
The proposed method achieved the state-of-the-art performance with an efficiency of 0.25 second per volume.
arXiv Detail & Related papers (2020-10-19T13:56:22Z) - Weakly Supervised Deep Nuclei Segmentation Using Partial Points
Annotation in Histopathology Images [51.893494939675314]
We propose a novel weakly supervised segmentation framework based on partial points annotation.
We show that our method can achieve competitive performance compared to the fully supervised counterpart and the state-of-the-art methods.
arXiv Detail & Related papers (2020-07-10T15:41:29Z) - 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.