Bitewing Radiography Semantic Segmentation Base on Conditional Generative Adversarial Nets
Currently, Segmentation of bitewing radiograpy images is a very challenging task. The focus of the study is to segment it into caries, enamel, dentin, pulp, crowns, restoration and root canal treatments. The main method of semantic segmentation of bitewing radiograpy images at this stage is the U-shaped deep convolution neural network, but its accuracy is low. in order to improve the accuracy of semantic segmentation of bitewing radiograpy images, this paper proposes the use of Conditional Generative Adversarial network (cGAN) combined with U-shaped network structure (U-Net) approach to semantic segmentation of bitewing radiograpy images. The experimental results show that the accuracy of cGAN combined with U-Net is 69.7%, which is 13.3% higher than the accuracy of u-shaped deep convolution neural network of 56.4%.
Code (0)
등록된 구현이 없습니다.
Tasks
Generative Adversarial NetworkSegmentationSemantic SegmentationMethods 이 논문이 사용한 방법론
Similar Papers 제목 키워드 기반
Interpretable and Interactive Deep Multiple Instance Learning for Dental Caries Classification in Bitewing X-rays
We propose a simple and efficient image classification architecture based on deep multiple instance learning, and apply it to the challenging task of caries detection in dental radiographs. Technically, our approach cont…
Classificationimage-classificationImage ClassificationMultiple Instance Learning+1Radious: Unveiling the Enigma of Dental Radiology with BEIT Adaptor and Mask2Former in Semantic Segmentation
X-ray images are the first steps for diagnosing and further treating dental problems. So, early diagnosis prevents the development and increase of oral and dental diseases. In this paper, we developed a semantic segmenta…
Image SegmentationSegmentationSemantic SegmentationPre-Training with Diffusion models for Dental Radiography segmentation
Medical radiography segmentation, and specifically dental radiography, is highly limited by the cost of labeling which requires specific expertise and labor-intensive annotations. In this work, we propose a straightforwa…
DenoisingSegmentationSemantic SegmentationAI-Dentify: Deep learning for proximal caries detection on bitewing x-ray -- HUNT4 Oral Health Study
Background: Dental caries diagnosis requires the manual inspection of diagnostic bitewing images of the patient, followed by a visual inspection and probing of the identified dental pieces with potential lesions. Yet the…
Deep LearningDiagnosticobject-detectionObject DetectionDetection of Tooth caries in Bitewing Radiographs using Deep Learning
We develop a Computer Aided Diagnosis (CAD) system, which enhances the performance of dentists in detecting wide range of dental caries. The CAD System achieves this by acting as a second opinion for the dentists with wa…
Deep LearningSensitivity