Abstract: The present disclosure relates to a computer implemented method for automatically generating and displaying a 2D image derived from 3D image data representing a portion of a patient's maxillofacial anatomy. The method comprises the steps of receiving 3D image data comprising a voxel image volume representing the maxillofacial anatomy, the voxel image volume including teeth of the patient and each voxel of the voxel image volume being associated with a radiation intensity value; detecting one or more anatomical landmarks in said voxel image volume using a first artificial neural network; determining a crown center position for each tooth of multiple teeth included in the voxel image volume with the detected anatomical landmarks as reference points and using a second artificial neural network; and generating the 2D image as a reslice image from the 3D image data based on at least one crown center position.

Abstract: Methods, systems, and computer programs are disclosed for segmenting, from an image, a tooth's pulp region comprising a pulp chamber and root canals. Curvature-based shape recognition is performed at different spatial scales using smoothed versions of the image. An indication of a point or region is received, located in the pulp chamber and referred to as “seed”. The seed is used as initial segmentation mask. An update procedure, iteratively carried out, comprises: (i) determining candidate image elements for updating the segmentation mask, comprising: (i?1) in the first n iteration(s), a grayscale thresholding taking as reference the current segmentation mask; and, (i?2) in at least one iteration, taking the curvature-based shape recognition into account; (ii) retaining, among the candidate image elements, a region of connected candidate image elements that comprises the seed; and (iii) using the retained region to update the segmentation mask.

Abstract: Methods, systems, and computer programs are disclosed for segmenting, from an image, a tooth's pulp region comprising a pulp chamber and root canals. Curvature-based shape recognition is performed at different spatial scales using smoothed versions of the image. An indication of a point or region is received, located in the pulp chamber and referred to as “seed”. The seed is used as initial segmentation mask. An update procedure, iteratively carried out, comprises: (i) determining candidate image elements for updating the segmentation mask, comprising: (i?1) in the first n iteration(s), a grayscale thresholding taking as reference the current segmentation mask; and, (i?2) in at least one iteration, taking the curvature-based shape recognition into account; (ii) retaining, among the candidate image elements, a region of connected candidate image elements that comprises the seed; and (iii) using the retained region to update the segmentation mask.