An efficient method to automate tooth identification and 3D bounding box extraction from Cone Beam CT Images
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Abstract (arXiv)
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.
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References · 8
- 2015U-Net: Convolutional Networks for Biomedical Image Segmentationcited by 19,604
- 2016V-Net: Fully Convolutional Neural Networks for Volumetric Medical Image Segmentationcited by 2,284
- 20163D U-Net: Learning Dense Volumetric Segmentation from Sparse Annotationcited by 1,471
- 2022YOLOv7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectorscited by 978
- 2016Simple Online and Realtime Trackingcited by 862
- 2021A fully automated method for 3D individual tooth identification and segmentation in dental CBCTcited by 18
- 2020Pose-Aware Instance Segmentation Framework from Cone Beam CT Images for Tooth Segmentationcited by 10
- 2018Dental pathology detection in 3D cone-beam CTcited by 1
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