Machine learning dental segmentation system and methods using graph-based approaches

Patent metadata

Field	Value
Publication number	US-12579656-B2
Application number	US-202217671406-A
Country	US
Kind code	B2
Filing date	Feb 14, 2022
Priority date	Feb 12, 2021
Publication date	Mar 17, 2026
Grant date	Mar 17, 2026

Abstract

Provided herein are systems and methods for automatically segmenting a 3D model of a patient's teeth. A patient's dentition may be scanned. The scan data may be converted into a 3D model, including a graph-based representation of the 3D model. The graph-based representation can be input into a machine learning model to train the machine learning model to segment the 3D model into individual dental components. Trained machine learning models can also be used to segment graph-based representations of a 3D model of a patient's teeth.

First claim

What is claimed is:

1 . A method of training a machine learning model to segment a 3D dental model, the method comprising:
receiving, in a computing device, a three-dimensional (3D) mesh of a patient's dentition; extracting mesh features from the 3D mesh, wherein the mesh features include face adjacency matrices describing adjacent and non-adjacent faces with respect to the 3D mesh; creating a graph-based representation of the 3D mesh with the extracted mesh features wherein the face adjacency matrices of the extracted mesh features are edges of the graph-based representation; and applying the mesh features and/or the graph-based representation to train a machine learning model of the computing device to recognize graph-based segmentation elements corresponding to segmentation of the patient's dentition.

2 . The method of claim 1 , wherein the mesh features further include mesh faces of the 3D mesh.

3 . The method of claim 1 , wherein creating the graph-based representation further comprises using mesh faces as nodes of the graph-based representation.

4 . The method of claim 3 , further comprising storing the graph-based representation in a memory of the computing device.

5 . The method of claim 1 , wherein the mesh features can be one or more of a face center position, a face normal vector, a local curvature, and a face area.

6 . The method of claim 1 , further comprising segmenting the graph-based representation into individual dental components with the machine learning model.

7 . The method of claim 1 , wherein the 3D mesh comprises a scan of the patient's dentition.

8 . The method of claim 1 , further comprising repeating the receiving, extracting, creating, and applying steps for a plurality of 3D meshes of patients' dentitions.

9 . The method of claim 1 , wherein the machine learning model is trained to construct a semantic segmentation network in which multiple objects of the same class are treated as a single entity.

10 . The method of claim 1 , wherein the machine learning model is trained to construct an instance segmentation network in which multiple objects of the same class are treated as distinct individual objects or instances.

11 . A method of training a machine learning model to segment a 3D dental model, the method comprising:
receiving, in a computing device, a three-dimensional (3D) mesh of a patient's dentition; creating, in the computing device, a graph-based representation of the 3 D mesh that represents the patient's dentition; receiving, in the computing device, a ground truth input comprising a manual segmentation of the 3D mesh, wherein the ground truth input identifies individual teeth, gingiva, and interproximal spaces; and training a machine learning model of the computing device to produce a segmentation output that attempts to achieve the ground truth input.

12 . The method of claim 11 , further comprising segmenting the graph-based representation into individual dental components with the trained machine learning model.

13 . The method of claim 11 , further comprising identifying interproximal spaces between teeth with the trained machine learning model.

14 . The method of claim 11 , further comprising identifying individual teeth with the trained machine learning model.

15 . The method of claim 11 , further comprising repeating the receiving, creating, receiving, and training steps for a plurality of 3D meshes of patients' dentitions.

16 . The method of claim 11 , wherein training the machine learning model comprises adjusting weights of the machine learning model to minimize an error between the ground truth input and the segmentation output.

17 . A method of segmenting a 3D dental model, the method comprising:
receiving, in a computing device, a three-dimensional (3D) mesh of a patient's dentition; creating a graph-based representation of the 3D mesh; applying the graph-based representation to a trained machine learning model of the computing device to recognize segmentation elements corresponding to segmentation of the patient's dentition, wherein the graph-based representation includes mesh features with face adjacency matrices describing adjacent and non-adjacent faces with respect to the 3D mesh as edges of the graph-based representation; and outputting a segmented 3D model of the patient's dentition.

18 . A method of training a machine learning model to segment a 3D dental model, the method comprising:
receiving, in a computing device, a three-dimensional (3D) mesh of a patient's dentition; extracting mesh features from the 3D mesh, wherein the mesh features include face adjacency matrices describing adjacent and non-adjacent faces with respect to the 3D mesh; coarsening the 3D mesh to reduce a size of the mesh; creating a graph-based representation of the coarsened 3D mesh with the extracted mesh features using face adjacency matrices as edges of the graph-based representation; and applying the mesh features and/or the graph-based representation to train a machine learning model of the computing device to recognize graph-based segmentation elements corresponding to segmentation of the patient's dentition.

19 . The method of claim 18 , wherein coarsening the 3D mesh comprises:
computing a cosine similarity of the mesh features; eliminating mesh features with a cosine similarity below a first threshold; and applying pooling to remaining mesh features.

20 . The method of claim 19 , wherein the pooling comprises Graclus pooling.

21 . The method of claim 19 , wherein the pooling is repeated until a number of remaining nodes in the 3D mesh is less than or equal to a second threshold.

22 . The method of claim 19 , wherein the first threshold comprises 0.995.

23 . The method of claim 21 , wherein the second threshold comprises 200,000.

Assignees

• Align Technology Inc

Inventors

• Cramer Christopher E
• Agniashvili Pavel
• Brown Chad Clayton

Classifications

• G06V10/82

  using neural networks · CPC title

• G06T2207/20084

  Artificial neural networks [ANN] · CPC title

• G06T2207/20081

  Training; Learning · CPC title

• G06T2207/30036

  Dental; Teeth · CPC title

• A61C13/34

  Making or working of models, e.g. preliminary castings, trial dentures; Dowel pins [4] · CPC title