Abstract: A computer-implemented method for automated classification of 3D image data of teeth includes a computer receiving one or more of 3D image data sets where a set defines an image volume of voxels representing 3D tooth structures within the image volume associated with a 3D coordinate system. The computer pre-processes each of the data sets and provides each of the pre-processed data sets to the input of a trained deep neural network. The neural network classifies each of the voxels within a 3D image data set on the basis of a plurality of candidate tooth labels of the dentition. Classifying a 3D image data set includes generating for at least part of the voxels of the data set a candidate tooth label activation value associated with a candidate tooth label defining the likelihood that the labelled data point represents a tooth type as indicated by the candidate tooth label.

Abstract: A computer-implemented method for correction of a voxel representation of metal affected x-ray data. The method comprises a first 3D deep neural network receiving an initial voxel representation of x-ray data at its input and generating a voxel map at its output, the map identifying voxels of the initial voxel representation that belong to a region of voxels that are affected by metal. A second 3D deep neural network receives the initial voxel representation and the map generated by the first 3D deep neural network at its input and generating a corrected voxel representation, the corrected voxel representation including voxel estimations for voxels that are identified by the voxel map as being part of a metal affected region, the first 3D deep neural being trained on the basis of training data and reference data that include voxel representations of clinical x-ray data of a predetermined body part of a patient.

Abstract: A computer-implemented method for automated 3D root shape prediction comprising: receiving data defining at least one 3D representation of a tooth and processing the data including: transforming at least part of the data into a voxel representation of a crown; a pre-processor providing the representation of the crown to the input of the neural network trained on clinical representations of real teeth; the first neural network generating a representation of a root or a tooth from the crown, wherein the generation of the representation of the root or tooth includes: determining voxel activations in a voxel space of the output of the deep learning network, each activation representing a probability measure defining the probability that a voxel is part of the root or the tooth; and, determining whether a voxel activation is part of the root or the tooth by comparing the voxel activation with a voxel activation threshold value.

Abstract: A computer-implemented method for processing 3D image data of a dento-maxillofacial structure is described wherein the method may comprise the steps of: receiving 3D image data defining a volume of voxels, a voxel being associated with a radiodensity value and a position in the volume and the voxels providing a 3D representation of a dento-maxillofacial structure; using the voxels of the 3D image data to determine one or more 3D positional features for input to a first deep neural network, a 3D positional feature defining information aggregated from the entire received 3D data set; and, the first deep neural network receiving the 3D image data and the one or more positional features at its input and using the one or more 3D positional features to classify at least part of the voxels of the 3D image data into jaw, teeth and/or nerve voxels.

Abstract: A computer-implemented method for processing 3D image data of a dento-maxillofacial structure is described wherein the method may comprise the steps of: receiving 3D image data defining a volume of voxels, a voxel being associated with a radiodensity value and a position in the volume and the voxels providing a 3D representation of a dento-maxillofacial structure; using the voxels of the 3D image data to determine one or more 3D positional features for input to a first deep neural network, a 3D positional feature defining information aggregated from the entire received 3D data set; and, the first deep neural network receiving the 3D image data and the one or more positional features at its input and using the one or more 3D positional features to classify at least part of the voxels of the 3D image data into jaw, teeth and/or nerve voxels.