Segmentation of 3D Dental Images Using Deep Learning

• URL: http://arxiv.org/abs/2207.09582v2
• Date: Thu, 21 Jul 2022 10:02:03 GMT
• Title: Segmentation of 3D Dental Images Using Deep Learning
• Authors: Omar Boudraa
• Abstract summary: 3D image segmentation is a recent and crucial step in many medical analysis and recognition schemes. This paper provides a multi-phase Deep Learning-based system that hybridizes various efficient methods in order to get the best 3D segmentation output.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: 3D image segmentation is a recent and crucial step in many medical analysis and recognition schemes. In fact, it represents a relevant research subject and a fundamental challenge due to its importance and influence. This paper provides a multi-phase Deep Learning-based system that hybridizes various efficient methods in order to get the best 3D segmentation output. First, to reduce the amount of data and accelerate the processing time, the application of Decimate compression technique is suggested and justified. We then use a CNN model to segment dental images into fifteen separated classes. In the end, a special KNN-based transformation is applied for the purpose of removing isolated meshes and of correcting dental forms. Experimentations demonstrate the precision and the robustness of the selected framework applied to 3D dental images within a private clinical benchmark.

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)
• Discriminative Hamiltonian Variational Autoencoder for Accurate Tumor Segmentation in Data-Scarce Regimes [2.8498944632323755]
  We propose an end-to-end hybrid architecture for medical image segmentation. We use Hamiltonian Variational Autoencoders (HVAE) and a discriminative regularization to improve the quality of generated images. Our architecture operates on a slice-by-slice basis to segment 3D volumes, capitilizing on the richly augmented dataset.
  arXiv  Detail & Related papers  (2024-06-17T15:42:08Z)
• Disruptive Autoencoders: Leveraging Low-level features for 3D Medical Image Pre-training [51.16994853817024]
  This work focuses on designing an effective pre-training framework for 3D radiology images. We introduce Disruptive Autoencoders, a pre-training framework that attempts to reconstruct the original image from disruptions created by a combination of local masking and low-level perturbations. The proposed pre-training framework is tested across multiple downstream tasks and achieves state-of-the-art performance.
  arXiv  Detail & Related papers  (2023-07-31T17:59:42Z)
• PCDAL: A Perturbation Consistency-Driven Active Learning Approach for Medical Image Segmentation and Classification [12.560273908522714]
  Supervised learning deeply relies on large-scale annotated data, which is expensive, time-consuming, and impractical to acquire in medical imaging applications. Active Learning (AL) methods have been widely applied in natural image classification tasks to reduce annotation costs. We propose an AL-based method that can be simultaneously applied to 2D medical image classification, segmentation, and 3D medical image segmentation tasks.
  arXiv  Detail & Related papers  (2023-06-29T13:11:46Z)
• Cascaded Robust Learning at Imperfect Labels for Chest X-ray Segmentation [61.09321488002978]
  We present a novel cascaded robust learning framework for chest X-ray segmentation with imperfect annotation. Our model consists of three independent network, which can effectively learn useful information from the peer networks. Our methods could achieve a significant improvement on the accuracy in segmentation tasks compared to the previous methods.
  arXiv  Detail & Related papers  (2021-04-05T15:50:16Z)
• A fully automated method for 3D individual tooth identification and segmentation in dental CBCT [1.567576360103422]
  This paper proposes a fully automated method of identifying and segmenting 3D individual teeth from dental CBCT images. The proposed method addresses the aforementioned difficulty by developing a deep learning-based hierarchical multi-step model. Experimental results showed that the proposed method achieved an F1-score of 93.35% for tooth identification and a Dice similarity coefficient of 94.79% for individual 3D tooth segmentation.
  arXiv  Detail & Related papers  (2021-02-11T15:07:23Z)
• 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)
• Deep Q-Network-Driven Catheter Segmentation in 3D US by Hybrid Constrained Semi-Supervised Learning and Dual-UNet [74.22397862400177]
  We propose a novel catheter segmentation approach, which requests fewer annotations than the supervised learning method. Our scheme considers a deep Q learning as the pre-localization step, which avoids voxel-level annotation. With the detected catheter, patch-based Dual-UNet is applied to segment the catheter in 3D volumetric data.
  arXiv  Detail & Related papers  (2020-06-25T21:10:04Z)
• Pathological Retinal Region Segmentation From OCT Images Using Geometric Relation Based Augmentation [84.7571086566595]
  We propose improvements over previous GAN-based medical image synthesis methods by jointly encoding the intrinsic relationship of geometry and shape. The proposed method outperforms state-of-the-art segmentation methods on the public RETOUCH dataset having images captured from different acquisition procedures.
  arXiv  Detail & Related papers  (2020-03-31T11:50:43Z)
• 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.