Enregistré dans:
| Auteurs principaux: | , , , , , , , |
|---|---|
| Format: | Preprint |
| Publié: |
2024
|
| Sujets: | |
| Accès en ligne: | https://arxiv.org/abs/2407.05892 |
| Tags: |
Ajouter un tag
Pas de tags, Soyez le premier à ajouter un tag!
|
| _version_ | 1866917717442822144 |
|---|---|
| author | Botella, Ignacio Garrido Águeda, Ignacio Arranz Carmona, Juan Carlos Armenteros Vorontsov, Oleg Robledo, Fernando Bayón Solovykh, Evgeny Andreevich, Obrubov Aleksandr Barriuso, Adrián Alonso |
| author_facet | Botella, Ignacio Garrido Águeda, Ignacio Arranz Carmona, Juan Carlos Armenteros Vorontsov, Oleg Robledo, Fernando Bayón Solovykh, Evgeny Andreevich, Obrubov Aleksandr Barriuso, Adrián Alonso |
| contents | Accurate identification, localization, and segregation of teeth from Cone Beam Computed Tomography (CBCT) images are essential for analyzing dental pathologies. Modeling an individual tooth can be challenging and intricate to accomplish, especially when fillings and other restorations introduce artifacts. This paper proposes a method for automatically detecting, identifying, and extracting teeth from CBCT images. Our approach involves dividing the three-dimensional images into axial slices for image detection. Teeth are pinpointed and labeled using a single-stage object detector. Subsequently, bounding boxes are delineated and identified to create three-dimensional representations of each tooth. The proposed solution has been successfully integrated into the dental analysis tool Dentomo. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2407_05892 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | An efficient method to automate tooth identification and 3D bounding box extraction from Cone Beam CT Images Botella, Ignacio Garrido Águeda, Ignacio Arranz Carmona, Juan Carlos Armenteros Vorontsov, Oleg Robledo, Fernando Bayón Solovykh, Evgeny Andreevich, Obrubov Aleksandr Barriuso, Adrián Alonso Image and Video Processing Artificial Intelligence Computer Vision and Pattern Recognition Accurate identification, localization, and segregation of teeth from Cone Beam Computed Tomography (CBCT) images are essential for analyzing dental pathologies. Modeling an individual tooth can be challenging and intricate to accomplish, especially when fillings and other restorations introduce artifacts. This paper proposes a method for automatically detecting, identifying, and extracting teeth from CBCT images. Our approach involves dividing the three-dimensional images into axial slices for image detection. Teeth are pinpointed and labeled using a single-stage object detector. Subsequently, bounding boxes are delineated and identified to create three-dimensional representations of each tooth. The proposed solution has been successfully integrated into the dental analysis tool Dentomo. |
| title | An efficient method to automate tooth identification and 3D bounding box extraction from Cone Beam CT Images |
| topic | Image and Video Processing Artificial Intelligence Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2407.05892 |