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    DEEP LEARNING FOR TOOTH DETECTION AND EVALUATION
    12.
    发明申请
    DEEP LEARNING FOR TOOTH DETECTION AND EVALUATION 有权

    公开(公告)号：US20210264611A1

    公开(公告)日：2021-08-26

    申请号：US17246504

    申请日：2021-04-30

    Applicant: Align Technology, Inc.

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

    IPC: G06T7/13 ,  G06K9/62 ,  G06K9/46 ,  G06K9/66 ,  G06T7/00

    Abstract: A method includes receiving an image of a face of a patient, the image including a depiction of teeth; processing the image of the face using one or more trained machine learning model, wherein the one or more trained machine learning model outputs a pixel-level classification of pixels in the image, the pixel level classification comprising a first set of pixels classified as being inside of a bounding shape that bounds a first plurality of teeth depicted in the image and a second set of pixels classified as being outside of the first bounding shape; cropping the image of the face of the patient, wherein the cropped image comprises depictions of the first plurality of teeth; and performing one or more operations on the cropped image of the face of the patient.

    Deep learning for tooth detection and evaluation
    13.
    发明授权
    Deep learning for tooth detection and evaluation 有权

    公开(公告)号：US10997727B2

    公开(公告)日：2021-05-04

    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 ,  G06K9/62 ,  G06K9/66 ,  G06K9/46 ,  G06T7/00

    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.

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