ArticleDiagnostics (Basel, Switzerland)2024
STSN-Net: Simultaneous Tooth Segmentation and Numbering Method in Crowded Environments with Deep Learning.
Article in Diagnostics (Basel, Switzerland), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
8 citing papers in PubMed.
- Tooth X-Ray Image Segmentation Based on ResU-Net with Coordinate Attention and Boundary-Aware Mechanisms.Sensors (Basel, Switzerland) · 2026Article
- PXseg: automatic tooth segmentation, numbering and abnormal morphology detection based on CBCT and panoramic radiographs.BMC oral health · 2025Article
- Three-Dimensional Semantic Segmentation of Palatal Rugae and Maxillary Teeth and Motion Evaluation of Orthodontically Treated Teeth Using Convolutional Neural Networks.Diagnostics (Basel, Switzerland) · 2025Article
- A novel deep learning-based model for automated tooth detection and numbering in mixed and permanent dentition in occlusal photographs.BMC oral health · 2025Article
- Evaluation by dental professionals of an artificial intelligence-based application to measure alveolar bone loss.BMC oral health · 2025Article
- A Semi-Supervised Transformer-Based Deep Learning Framework for Automated Tooth Segmentation and Identification on Panoramic Radiographs.Diagnostics (Basel, Switzerland) · 2024Article
- Evaluation of the Alveolar Crest and Cemento-Enamel Junction in Periodontitis Using Object Detection on Periapical Radiographs.Diagnostics (Basel, Switzerland) · 2024Article
- DeMambaNet: Deformable Convolution and Mamba Integration Network for High-Precision Segmentation of Ambiguously Defined Dental Radicular Boundaries.Sensors (Basel, Switzerland) · 2024Article
Corrections and comments
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Authors and funding
10 authors.
Funding
Abstract
Accurate tooth segmentation and numbering are the cornerstones of efficient automatic dental diagnosis and treatment. In this paper, a multitask learning architecture has been proposed for accurate tooth segmentation and numbering in panoramic X-ray images. A graph convolution network was applied for the automatic annotation of the target region, a modified convolutional neural network-based detection subnetwork (DSN) was used for tooth recognition and boundary regression, and an effective region segmentation subnetwork (RSSN) was used for region segmentation. The features extracted using RSSN and DSN were fused to optimize the quality of boundary regression, which provided impressive results for multiple evaluation metrics. Specifically, the proposed framework achieved a top F1 score of 0.9849, a top Dice metric score of 0.9629, and an mAP (IOU = 0.5) score of 0.9810. This framework holds great promise for enhancing the clinical efficiency of dentists in tooth segmentation and numbering tasks.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.