Instance Segmentation and Teeth Classification in Panoramic X-rays

• URL: http://arxiv.org/abs/2406.03747v1
• Date: Thu, 6 Jun 2024 04:57:29 GMT
• Title: Instance Segmentation and Teeth Classification in Panoramic X-rays
• Authors: Devichand Budagam, Ayush Kumar, Sayan Ghosh, Anuj Shrivastav, Azamat Zhanatuly Imanbayev, Iskander Rafailovich Akhmetov, Dmitrii Kaplun, Sergey Antonov, Artem Rychenkov, Gleb Cyganov, Aleksandr Sinitca,
• Abstract summary: Teeth segmentation and recognition are critical in various dental applications and dental diagnosis. This article offers a pipeline of two deep learning models, U-Net and YOLOv8, which results in BB-UNet. We have improved the quality and reliability of teeth segmentation by utilising the YOLOv8 and U-Net capabilities.
• Score: 35.8246552579468
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Teeth segmentation and recognition are critical in various dental applications and dental diagnosis. Automatic and accurate segmentation approaches have been made possible by integrating deep learning models. Although teeth segmentation has been studied in the past, only some techniques were able to effectively classify and segment teeth simultaneously. This article offers a pipeline of two deep learning models, U-Net and YOLOv8, which results in BB-UNet, a new architecture for the classification and segmentation of teeth on panoramic X-rays that is efficient and reliable. We have improved the quality and reliability of teeth segmentation by utilising the YOLOv8 and U-Net capabilities. The proposed networks have been evaluated using the mean average precision (mAP) and dice coefficient for YOLOv8 and BB-UNet, respectively. We have achieved a 3\% increase in mAP score for teeth classification compared to existing methods, and a 10-15\% increase in dice coefficient for teeth segmentation compared to U-Net across different categories of teeth. A new Dental dataset was created based on UFBA-UESC dataset with Bounding-Box and Polygon annotations of 425 dental panoramic X-rays. The findings of this research pave the way for a wider adoption of object detection models in the field of dental diagnosis.

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