The Automated Registration tool, AREG, was first presented at the NA-MIC project week #39. It aims to reduce the sources of error in the 3D image processing workflow by automating the orientation and registration of 3D Cone-Beam Computed Tomography. These methods combine classical algorithmic approaches and AI-based models trained and tested on de-identified CBCT volumetric images.
The registration method is based on an automatic tool, AMASS, available in the extension SlicerAutomatedDentalTool, to perform a segmentation of the different regions of reference used for the regional voxel-based registration
The different methods for automatic orientation and registration of 3D CBCT scans rely on a combination of algorithmic and deep-learning techniques to perform both the orientation and the registration automatically. It also uses work that our group of researchers has already developed. Our Python-based algorithm and requires multiple libraries for the different image-processing tasks accomplished throughout the proposed method: SimpleITK, VTK, SimpleElastix. To implement these tools, we also used the Medical Open Network for Artificial Intelligence (MONAI) library, which is a PyTorch-based framework for medical image analysis. MONAI offers several advantages for our work, such as high performance, modularity, and interoperability with other libraries.