Course: 24678: Computer Vision for Engineers
Carnegie Mellon University, Pittsburgh
Fall 2022
Team Phoenix
Team Type: Research
Contributors: Aditya Bapat, Ajith Potluri, Ziyuan Wei, Monalisa Malani
Modern dental imagery has undergone significant changes as a result of technological advancements. However, for an accurate diagnosis, multiple imaging methods are required to ensure improved efficiency. This makes the application of dental imagery for predicting treatment, detailed diagnosis, and tooth segmentation laborious, computationally intensive, and expensive.For example, CBCT images, lack spatial resolution for elaborately depicting tooth geometry, or the intra-oral scans cannot observe tooth roots in their digital impressions. Moreover, artifacts introduced by image degradation prevent accurate analysis and are also considered one of the fundamental problems in dental imagery.
To overcome the common issue of spatial resolution in dental imagery and the laborious process of tooth segmentation, we aim to utilize the advancements in computer vision technologies to build a 3D dental system. We will use DICOM format data to extract information in three-dimension and computer vision algorithms such as binarization, edge detection, and contour tracing with deep learning for tooth segmentation.Our proposed research framework will output a point cloud-based representation and consist of tooth segmentation information.
Our 3D dental system will be used toperform tooth decay analysis to showcase one of its many applications in dental diagnosis. We believe that this research project can be expanded to combine multiple dental imageries or generate a meshed 3D surface model to further increase data resolution.
pip install -r requirements.txt
conda env create -f conda_phoenix.yml
