Summary of Instance Segmentation and Teeth Classification in Panoramic X-rays, by Devichand Budagam et al.
Instance Segmentation and Teeth Classification in Panoramic X-rays
by Devichand Budagam, Ayush Kumar, Sayan Ghosh, Anuj Shrivastav, Azamat Zhanatuly Imanbayev, Iskander Rafailovich Akhmetov, Dmitrii Kaplun, Sergey Antonov, Artem Rychenkov, Gleb Cyganov, Aleksandr Sinitca
First submitted to arxiv on: 6 Jun 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed pipeline combines two deep learning models, U-Net and YOLOv8, to create BB-UNet, an architecture that simultaneously classifies and segments teeth on panoramic X-rays. This efficient and reliable approach improves the quality and reliability of teeth segmentation using the capabilities of both models. The pipeline is evaluated using mean average precision (mAP) for YOLOv8 and dice coefficient for BB-UNet, achieving a 3% increase in mAP score for teeth classification compared to existing methods and a 10-15% increase in dice coefficient for teeth segmentation across different categories of teeth. A new Dental dataset is created based on UFBA-UESC dataset with Bounding-Box and Polygon annotations of 425 dental panoramic X-rays, paving the way for wider adoption of object detection models in dental diagnosis. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Teeth are important in dental applications and diagnosis. Researchers have developed ways to automatically segment and recognize teeth using deep learning models. This article presents a new approach that combines two models, U-Net and YOLOv8, to classify and segment teeth on X-ray images. The new model is efficient and reliable, making it useful for dental diagnosis. The research also creates a new dataset with labeled images of teeth, which can help improve the accuracy of object detection in dentistry. |
Keywords
» Artificial intelligence » Bounding box » Classification » Deep learning » Mean average precision » Object detection » Unet
