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EP-4740186-A1 - AUTOMATIC IDENTIFICATION OF REGIONS OF INTEREST IN INTRAORAL SCANNING SYSTEM

EP4740186A1EP 4740186 A1EP4740186 A1EP 4740186A1EP-4740186-A1

Abstract

An intraoral scanning system (100), a method (300), and a device (800) for automatic identification of region(s) of interest in a 3D representation of an oral cavity. The method (300) comprises obtaining, using an intraoral scanner, a point cloud representing a 3D geometric surface of an oral cavity (310); identifying, based on the point cloud, at least one region of interest on the 3D geometric surface of the oral cavity (320); and displaying the 3D geometric surface of the oral cavity with the at least one region of interest highlighted or marked (330).

Inventors

  • WU, Houhang
  • LIN, Dafu
  • XIONG, Jingqi

Assignees

  • ALLIEDSTAR (SHANGHAI) MEDICAL TECHNOLOGY CO., LTD.

Dates

Publication Date
20260513
Application Date
20240920

Claims (20)

  1. A system comprising: an intraoral scanner; and a computing device coupled to the intraoral scanner and configured to: obtain, using the intraoral scanner, a point cloud representing a three-dimensional (3D) geometric surface of an oral cavity; identify, based on the point cloud, at least one region of interest on the 3D geometric surface of the oral cavity; and display the 3D geometric surface of the oral cavity with the at least one region of interest highlighted or marked.
  2. The system of claim 1, wherein the at least one region of interest includes at least one of: a tooth to be restored; dental caries; a malformed tooth; an implant sleeve; or a scan body.
  3. The system of claim 1, wherein, to identify the at least one region of interest on the 3D geometric surface of the oral cavity, the computing device is configured to: assign, using a deep learning-based model, data points in the point cloud with corresponding classifications.
  4. The system of claim 3, wherein, to identify the at least one region of interest on the 3D geometric surface of the oral cavity, the computing device is configured to: perform, before using the deep learning-based model, min-max normalization and mean standardization for coordinates and normal vectors of the data points in the point cloud.
  5. The system of claim 3 or 4, wherein the computing device is further configured to sample a subset of data points in the point cloud for classification.
  6. The system of any of claims 3 to 5, to identify the at least one region of interest on the 3D geometric surface of the oral cavity, the computing device is configured to: obtain a plurality of connectivity domains based on the data points with the same classification; and filter the connectivity domains with at least one predetermined size to obtain the at least one region of interest.
  7. The system of any of claims 3 to 6, wherein the computing device is further configured to: augment a training dataset of the deep learning-based model by combining data points corresponding to prepared teeth or scan bodies with normal teeth data from different point cloud.
  8. The system of any of claims 3 to 7, wherein the computing device is further configured to: augment a training dataset of the deep learning-based model by applying at least one rotation matrix to original point cloud.
  9. The system of any of claims 1 to 8, wherein, to display the 3D geometric surface of the oral cavity with the at least one region of interest highlighted or marked, the computing device is configured to: display the 3D geometric surface of the oral cavity with at least one 3D cylinder each containing one of the at least one region of interest.
  10. The system of any of claims 1 to 9, wherein the computing device is further configured to: obtain, using the intraoral scanner, an enhanced 3D representation of the at least one region of interest.
  11. A method comprising: obtaining, using an intraoral scanner, a point cloud representing a three-dimensional (3D) geometric surface of an oral cavity; identifying, based on the point cloud, at least one region of interest on the 3D geometric surface of the oral cavity; and displaying the 3D geometric surface of the oral cavity with the at least one region of interest highlighted or marked.
  12. The method of claim 11, wherein the at least one region of interest includes at least one of: a tooth to be restored; dental caries; a malformed tooth; an implant sleeve; or a scan body.
  13. The method of claim 11, wherein identifying the at least one region of interest on the 3D geometric surface of the oral cavity comprises: assigning, using a deep learning-based model, data points in the point cloud with corresponding classifications.
  14. The method of claim 13, wherein identifying the at least one region of interest on the 3D geometric surface of the oral cavity comprises: performing, before using the deep learning-based model, min-max normalization and mean standardization for coordinates and normal vectors of the data points in the point cloud.
  15. The method of claim 13 or 14, further comprising: sampling a subset of data points in the point cloud for classification.
  16. The method of any of claims 13 to 15, wherein identifying the at least one region of interest on the 3D geometric surface of the oral cavity comprises: obtaining a plurality of connectivity domains based on the data points with the same classification; and filtering the connectivity domains with at least one predetermined size to obtain the at least one region of interest.
  17. The method of any of claims 13 to 16, further comprising: augmenting a training dataset of the deep learning-based model by at least one of: combining data points corresponding to prepared teeth or scan bodies with normal teeth data from different point cloud; or applying at least one rotation matrix to original point cloud.
  18. The method of any of claims 11 to 17, wherein displaying the 3D geometric surface of the oral cavity with the at least one region of interest highlighted or marked comprises: displaying the 3D geometric surface of the oral cavity with at least one 3D cylinder each containing one of the at least one region of interest.
  19. The method of any of claims 11 to 18, further comprising: obtaining, using the intraoral scanner, an enhanced 3D representation of the at least one region of interest.
  20. A device, comprising: a processor; and a memory storing executable instructions that, in response to execution by the processor, cause the device to at least: obtain, using an intraoral scanner, a point cloud representing a three-dimensional (3D) geometric surface of an oral cavity; identify, based on the point cloud, at least one region of interest on the 3D geometric surface of the oral cavity; and display the 3D geometric surface of the oral cavity with the at least one region of interest highlighted or marked.

Description

AUTOMATIC IDENTIFICATION OF REGIONS OF INTEREST IN INTRAORAL SCANNING SYSTEM FIELD Example embodiments of the present disclosure generally relate to the field of intraoral scanning, and in particular, to an intraoral scanning system, a method, a device and a non-transitory computer-readable medium for automatic identification of regions of interest (ROI) in an intraoral scanning system. BACKGROUND Intraoral scanning refers to the process of digitally capturing detailed three-dimensional (3D) images of the structures inside the mouth, including teeth, gums, and surrounding tissues, using specialized optical technology. Unlike traditional dental impressions, which involve the use of physical molds or trays filled with impression material, intraoral scanning provides a non-invasive and comfortable alternative for both patients and dental professionals. During an intraoral scan, a dental professional uses a handheld device (i.e., an intraoral scanner) equipped with cameras and sensors to capture multiple images of the oral cavity from various angles. These images are transmitted to a computer station and then rapidly stitched together using sophisticated algorithms to create a 3D representation of the patient’s teeth and soft tissues, which could be visualized at the computer station and viewed by the dental professional. In addition to presenting the 3D representation to the dental professional in the form of point clouds or meshes, various workflow interactions are involved. These interactions typically require users to complete multiple mouse movements or clicks (for example, identifying teeth to be restored or scan bodies) . For dental professionals using the intraoral scanner to collect patient data, operating the mouse while wearing gloves is very inconvenient. SUMMARY Example embodiments of the present disclosure relates to solutions for automatic identification of regions of interest (ROI) in an intraoral scanning system. In a first aspect, there is provided an intraoral scanning system. The system comprise an intraoral scanner; and a computing device coupled to the intraoral scanner and configured to:obtain, using the intraoral scanner, a point cloud representing a three-dimensional (3D) geometric surface of an oral cavity; identify, based on the point cloud, at least one region of interest on the 3D geometric surface of the oral cavity; and display the 3D geometric surface of the oral cavity with the at least one region of interest highlighted or marked. In some embodiments of the system, the at least one region of interest may include at least one of: a tooth to be restored; dental caries; a malformed tooth; an implant sleeve; or a scan body. In some embodiments of the system, to identify the at least one region of interest on the 3D geometric surface of the oral cavity, the computing device is configured to: assign, using a deep learning-based model, data points in the point cloud with corresponding classifications. In some embodiments of the system, to identify the at least one region of interest on the 3D geometric surface of the oral cavity, the computing device is configured to: perform, before using the deep learning-based model, min-max normalization and mean standardization for coordinates and normal vectors of the data points in the point cloud. In some embodiments of the system, the computing device is further configured to sample a subset of data points in the point cloud for classification. In some embodiments of the system, to identify the at least one region of interest on the 3D geometric surface of the oral cavity, the computing device is configured to: obtain a plurality of connectivity domains based on the data points with the same classification; and filter the connectivity domains with at least one predetermined size to obtain the at least one region of interest. In some embodiments of the system, the computing device is further configured to: augment a training dataset of the deep learning-based model by combining data points corresponding to prepared teeth or scan bodies with normal teeth data from different point cloud. In some embodiments of the system, the computing device is further configured to: augment a training dataset of the deep learning-based model by applying at least one rotation matrix to original point cloud. In some embodiments of the system, to display the 3D geometric surface of the oral cavity with the at least one region of interest highlighted or marked, the computing device is configured to: display the 3D geometric surface of the oral cavity with at least one 3D cylinder each containing one of the at least one region of interest. In some embodiments of the system, the computing device is further configured to: obtain, using the intraoral scanner, an enhanced 3D representation of the at least one region of interest. In a second aspect, there is provided a method. The method comprises: obtaining, using an intraoral scanner, a point cloud representing a thre