Method for automatic segmentation of a dental arch

A computing device comprising a non-transitory computer readable storage medium having program instructions stored therein, the instructions being executable by at least one processing core of a processing unit to cause the processing unit to perform a method for automatic segmentation of a dental arch, the device comprising:

• a computing module of the program instructions for acquiring a three-dimensional surface of the dental arch, configured to obtain a three-dimensional representation of the dental arch in a three-dimensional space, said three-dimensional representation comprising a set of three-dimensional points, referred to as vertices, forming vertices of polygonal, preferably triangular, faces;
• a computing module of the program instructions for generating two-dimensional virtual views from the three-dimensional representation, configured to determine the characteristics of the virtual views by determining a wireframe representing the general shape of the dental arch and by determining the characteristics of the virtual views by selecting virtual views distributed along the wireframe and directed towards the wireframe;
• a computing module of the program instructions for projecting the three-dimensional representation on each two-dimensional virtual view, configured to obtain, for each virtual view, an image representing each vertex and each polygonal face visible on the virtual view;
• a computing module of the program instructions for processing each image by a previously trained deep learning network, associating, with each pixel of each image, a probability vector, each index of the vector representing the probability of said pixel belonging to a class of dental tissues, from among different classes of dental tissues of a same size as the probability vector;
• a computing module of the program instructions for carrying out inverse projection of each image so as to assign to each vertex of the three-dimensional representation one or more pixels of the images wherein the vertex appears and to which it corresponds, and assigning to each vertex the probability vector(s) associated with said one or more pixels;
• and,
• a computing module of the program instructions for determining, for each vertex, the dental tissue class to which said vertex most probably belongs based on the probability vector(s) assigned to said vertex.