Intergrated Segmentation and Detection Models for Dentex Challenge 2023

• URL: http://arxiv.org/abs/2308.14161v2
• Date: Mon, 4 Sep 2023 03:11:36 GMT
• Title: Intergrated Segmentation and Detection Models for Dentex Challenge 2023
• Authors: Lanshan He, Yusheng Liu, Lisheng Wang
• Abstract summary: With the development of deep learning, auto detection of diseases from dental panoramic x-rays can help dentists to diagnose diseases more efficiently. The Dentex Challenge 2023 is a competition for automatic detection of abnormal teeth along with their enumeration ids from dental panoramic x-rays.
• Score: 2.1025078609239403
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Dental panoramic x-rays are commonly used in dental diagnosing. With the development of deep learning, auto detection of diseases from dental panoramic x-rays can help dentists to diagnose diseases more efficiently.The Dentex Challenge 2023 is a competition for automatic detection of abnormal teeth along with their enumeration ids from dental panoramic x-rays. In this paper, we propose a method integrating segmentation and detection models to detect abnormal teeth as well as obtain their enumeration ids.Our codes are available at https://github.com/xyzlancehe/DentexSegAndDet.

Related papers

• TSegFormer: 3D Tooth Segmentation in Intraoral Scans with Geometry Guided Transformer [47.18526074157094]
  Optical Intraoral Scanners (IOSs) are widely used in digital dentistry to provide detailed 3D information of dental crowns and the gingiva. Previous methods are error-prone at complicated boundaries and exhibit unsatisfactory results across patients. We propose TSegFormer which captures both local and global dependencies among different teeth and the gingiva in the IOS point clouds with a multi-task 3D transformer architecture.
  arXiv  Detail & Related papers  (2023-11-22T08:45:01Z)
• A Sequential Framework for Detection and Classification of Abnormal Teeth in Panoramic X-rays [1.8962225869778402]
  This paper describes our solution for the Dentalteethion and Diagnosis on Panoramic X-rays Challenge at MICCAI 2023. Our approach consists of a multi-step framework tailored to the task of detecting and classifying abnormal teeth.
  arXiv  Detail & Related papers  (2023-08-31T13:47:01Z)
• DETDet: Dual Ensemble Teeth Detection [0.0]
  The 2023 MICCAI DENTEX challenge aims to enhance the performance of dental panoramic X-ray diagnosis and enumeration. We introduce DETDet, a Dual Ensemble Teeth Detection network. We employ Mask-RCNN for the enumeration module and DINO for the diagnosis module.
  arXiv  Detail & Related papers  (2023-08-27T11:04:26Z)
• YOLOrtho -- A Unified Framework for Teeth Enumeration and Dental Disease Detection [4.136033167469768]
  YOLOrtho is a unified framework for teeth enumeration and dental disease detection. We develop our model on Dentex Challenge 2023 data, which consists of three distinct types of annotated data. To fully utilize the data and learn both teeth detection and disease identification simultaneously, we formulate diseases as attributes attached to their corresponding teeth.
  arXiv  Detail & Related papers  (2023-08-11T06:54:55Z)
• Construction of unbiased dental template and parametric dental model for precision digital dentistry [46.459289444783956]
  We develop an unbiased dental template by constructing an accurate dental atlas from CBCT images with guidance of teeth segmentation. A total of 159 CBCT images of real subjects are collected to perform the constructions.
  arXiv  Detail & Related papers  (2023-04-07T09:39:03Z)
• An Implicit Parametric Morphable Dental Model [79.29420177904022]
  We present the first parametric 3D morphable dental model for both teeth and gum. It is based on a component-wise representation for each tooth and the gum, together with a learnable latent code for each of such components. Our reconstruction quality is on par with the most advanced global implicit representations while enabling novel applications.
  arXiv  Detail & Related papers  (2022-11-21T12:23:54Z)
• Teeth3DS+: An Extended Benchmark for Intraoral 3D Scans Analysis [7.546387289692397]
  This article introduces Teeth3DS+, the first comprehensive public benchmark designed to advance the field of intraoral 3D scan analysis. The dataset includes at least 1,800 intraoral scans (containing 23,999 teeth) collected from 900 patients, covering both upper and lower jaws separately.
  arXiv  Detail & Related papers  (2022-10-12T11:18:35Z)
• OdontoAI: A human-in-the-loop labeled data set and an online platform to boost research on dental panoramic radiographs [53.67409169790872]
  This study addresses the construction of a public data set of dental panoramic radiographs. We benefit from the human-in-the-loop (HITL) concept to expedite the labeling procedure. Results demonstrate a 51% labeling time reduction using HITL, saving us more than 390 continuous working hours.
  arXiv  Detail & Related papers  (2022-03-29T18:57:23Z)
• Generative Residual Attention Network for Disease Detection [51.60842580044539]
  We present a novel approach for disease generation in X-rays using a conditional generative adversarial learning. We generate a corresponding radiology image in a target domain while preserving the identity of the patient. We then use the generated X-ray image in the target domain to augment our training to improve the detection performance.
  arXiv  Detail & Related papers  (2021-10-25T14:15:57Z)
• 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)
• PaXNet: Dental Caries Detection in Panoramic X-ray using Ensemble Transfer Learning and Capsule Classifier [8.164433158925593]
  In many cases, dental caries is hard to identify using x-rays due to different reasons such as low image quality. Here, we propose an automatic diagnosis system to detect dental caries in Panoramic images for the first time. The proposed model benefits from various pretrained deep learning models through transfer learning to extract relevant features from x-rays and uses a capsule network to draw prediction results.
  arXiv  Detail & Related papers  (2020-12-26T03:00:35Z)

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.