公开(公告)号：US20190180443A1

公开(公告)日：2019-06-13

申请号：US16182452

申请日：2018-11-06

Applicant: Align Technology, Inc.

Inventor： Ya Xue ,  Yingjie Li ,  Chao Shi ,  Aleksandr Anikin ,  Mikhail Toporkov ,  Aleksandr Sergeevich Karsakov

IPC: G06T7/13 ,  G06T7/00 ,  G06K9/62 ,  G06K9/66

Abstract: A machine learning model is trained to define bounding shapes around teeth in images. The machine learning model is trained by receiving a training dataset comprising a plurality of images, each image of the plurality of images comprising a face and a provided bounding shape around teeth in the image. The training dataset is input into an untrained machine learning model. The untrained machine learning model is trained based on the training dataset to generate a trained machine learning model that defines bounding shapes around teeth in images, wherein for an input image the trained machine learning model is to output a mask that defines a bounding shape around teeth of the input image, wherein the mask indicates, for each pixel of the input image, whether that pixel is inside of a defined bounding shape or is outside of the defined bounding shape.

公开(公告)号：US11996181B2

公开(公告)日：2024-05-28

申请号：US16010087

申请日：2018-06-15

Applicant: ALIGN TECHNOLOGY, INC.

Inventor： Ya Xue ,  Jeeyoung Choi ,  Justin B. Moore ,  Anton Spiridonov

IPC: A61C7/00 ,  A61C7/08 ,  A61C9/00 ,  G06F18/21 ,  G06F18/2413 ,  G06F18/243 ,  G06T7/10 ,  G06T7/66 ,  G16H30/40 ,  G06N3/08 ,  G16H50/20 ,  G16H50/50

CPC classification number: G16H30/40 ,  A61C7/002 ,  A61C7/08 ,  A61C9/0053 ,  G06F18/21 ,  G06F18/24147 ,  G06F18/24323 ,  G06T7/10 ,  G06T7/66 ,  G06N3/08 ,  G06T2207/20081 ,  G06T2207/20084 ,  G06T2207/20164 ,  G06T2207/30036 ,  G16H50/20 ,  G16H50/50

Abstract: Provided herein are systems and methods for detecting the eruption state (e.g., tooth type and/or eruption status) of a target tooth. A patient's dentition may be scanned and/or segmented. A target tooth may be identified. Dental features, principal component analysis (PCA) features, and/or other features may be extracted and compared to those of other teeth, such as those obtained through automated machine learning systems. A detector can identify and/or output the eruption state of the target tooth, such as whether the target tooth is a fully erupted primary tooth, a permanent partially erupted/un-erupted tooth, or a fully erupted permanent tooth.