A computer-implemented method for processing 3D data representing a dento-maxillofacial structure comprising: receiving 3D data, the 3D data including a first voxel representation of the dento-maxillofacial structure, a voxel being associated with a radiation intensity value, the voxels of the first voxel representation defining an image volume; using the 3D data to determine one or more 3D positional features associated with the first voxel representation of the dento-maxillofacial structure, a 3D positional feature defining information about positions of voxels of the first voxel representation relative to a position of a dental reference plane or a position of one or more dental reference objects in the image volume; providing the 3D data and the one or more 3D positional features to the input of a first 3D deep neural network, DNN, the first 3D DNN comprising first and second 3D convolutional neural network, CNN, layers (406,408), the first 3D CNN layers and second 3D CNN layers being connected to at least one fully connected 3D CNN layer and being configured to process first and associated second blocks of voxels (401 1,2) of the 3D data respectively, a first block of voxels (401 1) having the same or substantially the same centre point in the image volume as an associated second block of voxels (401 2), the second block of voxels representing a volume in real-world dimensions that is larger than a volume in real-world dimensions represented by the first block of voxels, the second 3D CNN layers being configured to determine contextual information associated with voxels of the first blocks of voxels and the at least one fully connected 3D CNN layer being configured to generate voxel activations, a voxel activation representing a probability that a voxel of the 3D data belongs to one of a plurality of dental structure classes, the first 3D DNN being trained based on a training set, the training set including 3D data including voxel representations of dento-maxillofacial structures and one or more 3D positional features derived from the 3D image data of the training set; and, receiving voxel activations for voxels of the 3D data from the output of the first 3D DNN and classifying the voxels based on the voxel activations and, optionally, determining a voxel representation of at least one of the plurality of dental structure classes based on the classified voxels. receiving 3D data, the 3D data including a first voxel representation of the dento-maxillofacial structure, a voxel being associated with a radiation intensity value, the voxels of the first voxel representation defining an image volume; using the 3D data to determine one or more 3D positional features associated with the first voxel representation of the dento-maxillofacial structure, a 3D positional feature defining information about positions of voxels of the first voxel representation relative to a position of a dental reference plane or a position of one or more dental reference objects in the image volume; providing the 3D data and the one or more 3D positional features to the input of a first 3D deep neural network, DNN, the first 3D DNN comprising first and second 3D convolutional neural network, CNN, layers (406,408), the first 3D CNN layers and second 3D CNN layers being connected to at least one fully connected 3D CNN layer and being configured to process first and associated second blocks of voxels (401 1,2) of the 3D data respectively, a first block of voxels (401 1) having the same or substantially the same centre point in the image volume as an associated second block of voxels (401 2), the second block of voxels representing a volume in real-world dimensions that is larger than a volume in real-world dimensions represented by the first block of voxels, the second 3D CNN layers being configured to determine contextual information associated with voxels of the first blocks of voxels and the at least one fully connected 3D CNN layer being configured to generate voxel activations, a voxel activation representing a probability that a voxel of the 3D data belongs to one of a plurality of dental structure classes, the first 3D DNN being trained based on a training set, the training set including 3D data including voxel representations of dento-maxillofacial structures and one or more 3D positional features derived from the 3D image data of the training set; and, receiving voxel activations for voxels of the 3D data from the output of the first 3D DNN and classifying the voxels based on the voxel activations and, optionally, determining a voxel representation of at least one of the plurality of dental structure classes based on the classified voxels.