公开(公告)号：CA3153131A1

公开(公告)日：2021-03-11

申请号：CA3153131

申请日：2020-09-04

Applicant: ALIGN TECHNOLOGY INC

Inventor： MAKIEWSKY IGOR ,  WEISS ASSAF ,  VOLGIN MAXIM ,  AGNIASHVILI PAVEL ,  BROWN CHAD CLAYTON ,  RASKHODCHIKOV ALEXANDER ,  KOPELMAN AVI ,  SABINA MICHAEL ,  BEN-DOV MOTI ,  FARKASH SHAI ,  MOSHE MAAYAN ,  SAPHIER OFER

IPC: A61C13/00 ,  A61C9/00 ,  G06N3/02 ,  G16H30/40

Abstract: Methods and systems are described that mark and/or correct margin lines and/or other features of dental sites. In one example a three-dimensional model of a dental site is generated from intraoral scan data of the dental site, the three-dimensional model comprising a representation of a preparation tooth. An image of the preparation tooth is received or generated, the image comprising a height map. Data from the image is processed using a trained machine learning model that has been trained to identify margin lines of preparation teeth, wherein the trained machine learning model outputs a probability map comprising, for each pixel in the image, a probability that the pixel depicts a margin line. The three-dimensional model of the dental site is then updated by marking the margin line on the representation of the preparation tooth based on the probability map.

公开(公告)号：AU2020341707A1

公开(公告)日：2022-03-24

申请号：AU2020341707

申请日：2020-09-04

Applicant: ALIGN TECHNOLOGY INC

Inventor： MAKIEWSKY IGOR ,  WEISS ASSAF ,  VOLGIN MAXIM ,  AGNIASHVILI PAVEL ,  BROWN CHAD CLAYTON ,  RASKHODCHIKOV ALEXANDER ,  KOPELMAN AVI ,  SABINA MICHAEL ,  BEN-DOV MOTI ,  FARKASH SHAI ,  MOSHE MAAYAN ,  SAPHIER OFER

IPC: G06T7/12 ,  G06T7/143

Abstract: Methods and systems are described that mark and/or correct margin lines and/or other features of dental sites. In one example a three-dimensional model of a dental site is generated from intraoral scan data of the dental site, the three-dimensional model comprising a representation of a preparation tooth. An image of the preparation tooth is received or generated, the image comprising a height map. Data from the image is processed using a trained machine learning model that has been trained to identify margin lines of preparation teeth, wherein the trained machine learning model outputs a probability map comprising, for each pixel in the image, a probability that the pixel depicts a margin line. The three-dimensional model of the dental site is then updated by marking the margin line on the representation of the preparation tooth based on the probability map.