Method and system of teeth alignment based on simulating of crown and root movement

Abstract

The present disclosure relates to a dental image processing protocol for the design of dental aligners. Specifically, the dental image processing protocol aids in the determination of tooth movements during realignment, based on an initial position and a final position, and on characteristics of the periodontal environment. Therefore, planned tooth movements reflect both crown movement and root movement within biological structures of the alveolar process.

1 . A method for generating an intermediate position of one or more teeth of a dental arch of a patient, comprising:
 classifying, via processing circuitry, pixels of one or more medical images into classes corresponding to biological structure types;   segmenting, via the processing circuitry, the classified pixels of the one or more medical images into biological structures;   rendering, via the processing circuitry, a three-dimensional model of the biological structures based on the segmented classified pixels;   determining, via the processing circuitry, one or more metrics, based upon the three-dimensional model, describing characteristics of a bone of the biological structures;   acquiring, via the processing circuitry, a final position of each of the one or more teeth of the dental arch based upon the three-dimensional model; and   generating, via the processing circuitry, the intermediate position of each of the one or more teeth of the patient based upon the one or more metrics and the acquired final position.   
     
     
         2 . The method according to  claim 1 , wherein the classifying further comprises:
 training, via the processing circuitry, a classifier on a training database; and   classifying, via the processing circuitry, the pixels of the one or more medical images based upon the classifier,   wherein the training database includes a corpus of reference medical images, each reference medical image comprising at least one identifiable reference biological structure associated in the training database with at least one corresponding description of the biological structure type.   
     
     
         3 . The method according to  claim 1 , wherein the intermediate position of each of the one or more teeth is determined based upon a position of an aspect of a proximate tooth of the one or more teeth of the dental arch. 
     
     
         4 . The method according to  claim 2 , wherein the training further comprises:
 training, via the processing circuitry, a first neural network according to a first dataset;   training, via the processing circuitry, a second neural network according to a second dataset, the second dataset comprising a plurality of classification predictions of the first neural network; and   generating, via the processing circuitry, the training database based upon a plurality of classification predictions of the second neural network.   
     
     
         5 . The method according to  claim 4 , wherein the second neural network is a fully convolutional neural network. 
     
     
         6 . The method according to  claim 1 , wherein one of the one or more metrics is a distance metric, the distance metric being defined as a distance between a surface of a root of one of the one or more teeth of the dental arch and a surface of an alveolar process. 
     
     
         7 . The method according to  claim 1 , wherein one of the one or more metrics is a density metric, the density metric being defined as a measure of mean intensity of voxels adjacent to a central voxel. 
     
     
         8 . A non-transitory computer-readable storage medium storing computer-readable instructions that, when executed by a computer having a processing circuitry, cause the computer to perform a method, the method comprising:
 classifying pixels of one or more medical images into classes corresponding to biological structure types;   segmenting the classified pixels of the one or more medical images into biological structures;   rendering a three-dimensional model of the biological structures based on the segmented classified pixels;   determining one or more metrics, based upon the three-dimensional model, describing characteristics of a bone of the biological structures;   acquiring a final position of each of the one or more teeth of the dental arch based upon the three-dimensional model; and   generating the intermediate position of each of the one or more teeth of the patient based upon the one or more metrics and the acquired final position.   
     
     
         9 . The method according to  claim 8 , wherein the classifying further comprises:
 training a classifier on a training database; and   classifying the pixels of the one or more medical images based upon the classifier,   wherein the training database includes a corpus of reference medical images, each reference medical image comprising at least one identifiable reference biological structure associated in the training database with at least one corresponding description of the biological structure type.   
     
     
         10 . The method according to  claim 8 , wherein the intermediate position of each of the one or more teeth is determined based upon a position of an aspect of a proximate tooth of the one or more teeth of the dental arch. 
     
     
         11 . The method according to  claim 9 , wherein the training further comprises:
 training a first neural network according to a first dataset;   training a second neural network according to a second dataset, the second dataset comprising a plurality of classification predictions of the first neural network; and   generating the training database based upon a plurality of classification predictions of the second neural network.   
     
     
         12 . The method according to  claim 11 , wherein the second neural network is a fully convolutional neural network. 
     
     
         13 . The method according to  claim 8 , wherein one of the one or more metrics is a distance metric, the distance metric being defined as a distance between a surface of a root of one of the one or more teeth of the dental arch and a surface of an alveolar process. 
     
     
         14 . The method according to  claim 8 , wherein one of the one or more metrics is a density metric, the density metric being defined as a measure of mean intensity of voxels adjacent to a central voxel. 
     
     
         15 . An apparatus for processing of dental images, comprising a processing circuitry configured to:
 classify pixels of one or more medical images into classes corresponding to biological structure types;   segment the classified pixels of the one or more medical images into biological structures;   render a three-dimensional model of the biological structures based on the segmented classified pixels;   determine one or more metrics, based upon the three-dimensional model, describing a bone of the biological structures;   acquire a final position of each of the one or more teeth of the dental arch based upon the three-dimensional model; and   generate the intermediate position of each of the one or more teeth of the patient based upon the one or more metrics and the acquired final position.   
     
     
         16 . The apparatus according to  claim 15 , wherein the processing circuitry is further configured to:
 train a classifier on a training database; and   classify the pixels of the one or more medical images based upon the classifier,   wherein the training database includes a corpus of reference medical images, each reference medical image comprising at least one identifiable reference biological structure associated in the training database with at least one corresponding description of the biological structure type.   
     
     
         17 . The apparatus according to  claim 15 , wherein the intermediate position of each of the one or more teeth is determined based upon a position of an aspect of a proximate tooth of the one or more teeth of the dental arch. 
     
     
         18 . The apparatus according to  claim 16 , wherein the training further comprises:
 training a first neural network according to a first dataset;   training a second neural network according to a second dataset, the second dataset comprising a plurality of classification predictions of the first neural network; and   generating the training database based upon a plurality of classification predictions of the second neural network.   
     
     
         19 . The apparatus according to  claim 15 , wherein one of the one or more metrics is a distance metric, the distance metric being defined as a distance between a surface of a root of one of the one or more teeth of the dental arch and a surface of an alveolar process. 
     
     
         20 . The apparatus according to  claim 15 , wherein one of the one or more metrics is a density metric, the density metric being defined as a measure of mean intensity of voxels adjacent to a central voxel.