OralBBNet: Spatially Guided Dental Segmentation of Panoramic X-Rays with Bounding Box Priors
Fuente:
arXiv
Salvato in:
| Autori principali: | , , , , , |
|---|---|
| Natura: | Preprint |
| Pubblicazione: |
2024
|
| Soggetti: | |
| Accesso online: | |
| Tags: |
Aggiungi Tag
Nessun Tag, puoi essere il primo ad aggiungerne!!
|
| _version_ | 1866908429608550400 |
|---|---|
| author | Budagam, Devichand Imanbayev, Azamat Zhanatuly Akhmetov, Iskander Rafailovich Sinitca, Aleksandr Antonov, Sergey Kaplun, Dmitrii |
| author_facet | Budagam, Devichand Imanbayev, Azamat Zhanatuly Akhmetov, Iskander Rafailovich Sinitca, Aleksandr Antonov, Sergey Kaplun, Dmitrii |
| contents | Teeth segmentation and recognition play a vital role in a variety of dental applications and diagnostic procedures. The integration of deep learning models has facilitated the development of precise and automated segmentation methods. Although prior research has explored teeth segmentation, not many methods have successfully performed tooth segmentation and detection simultaneously. This study presents UFBA-425, a dental dataset derived from the UFBA-UESC dataset, featuring bounding box and polygon annotations for 425 panoramic dental X-rays. In addition, this paper presents the OralBBNet architecture, which is based on the best segmentation and detection qualities of architectures such as U-Net and YOLOv8, respectively. OralBBNet is designed to improve the accuracy and robustness of tooth classification and segmentation on panoramic X-rays by leveraging the complementary strengths of U-Net and YOLOv8. Our approach achieved a 1-3% improvement in mean average precision (mAP) for tooth detection compared to existing techniques and a 15-20% improvement in the dice score for teeth segmentation over state-of-the-art (SOTA) solutions for various tooth categories and 2-4% improvement in the dice score compared to other SOTA segmentation architectures. The results of this study establish a foundation for the wider implementation of object detection models in dental diagnostics. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2406_03747 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | OralBBNet: Spatially Guided Dental Segmentation of Panoramic X-Rays with Bounding Box Priors Budagam, Devichand Imanbayev, Azamat Zhanatuly Akhmetov, Iskander Rafailovich Sinitca, Aleksandr Antonov, Sergey Kaplun, Dmitrii Computer Vision and Pattern Recognition Artificial Intelligence Machine Learning Teeth segmentation and recognition play a vital role in a variety of dental applications and diagnostic procedures. The integration of deep learning models has facilitated the development of precise and automated segmentation methods. Although prior research has explored teeth segmentation, not many methods have successfully performed tooth segmentation and detection simultaneously. This study presents UFBA-425, a dental dataset derived from the UFBA-UESC dataset, featuring bounding box and polygon annotations for 425 panoramic dental X-rays. In addition, this paper presents the OralBBNet architecture, which is based on the best segmentation and detection qualities of architectures such as U-Net and YOLOv8, respectively. OralBBNet is designed to improve the accuracy and robustness of tooth classification and segmentation on panoramic X-rays by leveraging the complementary strengths of U-Net and YOLOv8. Our approach achieved a 1-3% improvement in mean average precision (mAP) for tooth detection compared to existing techniques and a 15-20% improvement in the dice score for teeth segmentation over state-of-the-art (SOTA) solutions for various tooth categories and 2-4% improvement in the dice score compared to other SOTA segmentation architectures. The results of this study establish a foundation for the wider implementation of object detection models in dental diagnostics. |
| title | OralBBNet: Spatially Guided Dental Segmentation of Panoramic X-Rays with Bounding Box Priors |
| topic | Computer Vision and Pattern Recognition Artificial Intelligence Machine Learning |
| url | https://arxiv.org/abs/2406.03747 |