Align Technology, Inc.

Chao Shi

Yingjie Li

Andrey Bushev

Ya Xue

Yun Gao

Eric P Meyer

Zelko Relic

Michael Alan Stocks

Vladislav Andreevich Miryaha

Boris Aleksandrovich Vysokanov

Christopher Eric Cramer

Chad Clayton Brown

Aleksandr Sergeevich Karsakov

Aleksandr Anikin

Mikhail Toporkov

Methods for visualizing treatment outcomes are provided. In some embodiments, a method includes obtaining a first 2D image of a patient's teeth from a mobile device, and obtaining an arch model representing an outcome for the patient's teeth after orthodontic treatment. The method can include matching the patient's teeth in the first 2D image to corresponding teeth in the arch model, using a parametric model. The method can also include determining a target position for the patient's teeth, based on a position of the matched corresponding teeth in the arch model. The method can further include generating a second 2D image depicting the patient's teeth in the target position.

Methods for visualizing treatment outcomes

Embodiments include receiving image data comprising a representation of teeth, wherein the image data comprises a set of pixel locations for the teeth and depth values associated with the pixel locations in the set of pixel locations. The method further includes generating new image based on applying one or more functions to the image data, wherein the one or more functions adjust one or more color channels of the set of pixel locations based at least in part on the depth values.

Generating teeth images colored based on teeth depth

Provided herein are orthodontic systems and methods for automatically segmenting a person's teeth. Systems, methods and processes are provided to properly segment the teeth of a person's teeth from an image of the person's face showing at least some of the person's teeth (e.g., dental arch). Methods and systems are provided to automatically detect dental edges after a dental scan. Also described herein are methods and systems for generating a simulated image of the person's face from the final segmentation of the person's teeth in which the segmented teeth have been moved from their original position, including a new position based on an orthodontic treatment plan.

Tooth segmentation based on anatomical edge information

A processing device receives an image and calculates a sharpness metric for a region of interest of the image. The processing device determines whether the sharpness metric satisfies a sharpness criterion, and one or more additional operations are performed responsive to determining that the sharpness metric satisfies the sharpness criterion.

Selective image modification based on sharpness metric and image domain

Systems and methods of simulating dental treatments are disclosed. A method may include capturing a first 2D image of a patient's face, including the patient's teeth, building a parametric 3D model of the patient's teeth and gingiva based on the first 2D image, developing a simulated outcome of a dental treatment of the patient's teeth by rendering the parametric 3D model with the patient's teeth in one or more positions and/or orientations corresponding to the treatment goals of the dental treatment plan, and rendering a second 2D image of the patient's face with gingiva and teeth according to a simulated outcome of the dental treatment plan. As noted herein, the dental treatment plan may include orthodontic and/or restorative elements. The simulated outcome may correspond to estimated outcomes and/or intended outcomes of the dental treatment plan.

Visual presentation of gingival line generated based on 3D tooth model

In a technique to assess the blurriness of an image, an image of a face is received, the image including a depiction of lips. A processing device determines a region of interest in the image, wherein the region of interest comprises an area inside of the lips. The processing device applies a focus operator to the pixels within the region of interest, and calculates a sharpness metric for the region of interest using an output of the focus operator. The processing device determines whether the sharpness metric satisfies a sharpness criterion, and one or more additional operations are performed responsive to determining that the sharpness metric satisfies the sharpness criterion.

Domain specific image quality assessment

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

A method includes determining depth values associated with a first set of pixel locations in a first image of a mouth. One or more function is generated for one or more color channels based on intensities of the one or more color channels at the first set of pixel locations and depth values associated with the first set of pixel locations. Image data comprising a new representation of the teeth is received, wherein the image data comprises a second set of pixel locations and new depth values associated with the second set of pixel locations. A new image is generated based on the image data and the one or more functions.

Generation of images with tooth color determined using depth information

Methods for simulating orthodontic treatment are provided. In some embodiments, a method includes capturing a first representation, the first representation including a depiction of a patient's teeth. The method can include building a parametric model of the patient's teeth based on the first representation, using one or more case-specific parameters for one or more shapes associated with at least one of the patient's teeth. The method can also include simulating an outcome of a dental treatment plan for the patient's teeth to produce a simulated outcome of the dental treatment plan. The method can further include modifying the parametric model to provide a modified model representing the simulated outcome of the dental treatment plan.

Methods for simulating orthodontic treatment

A method includes determining depth values associated with a first set of pixel locations in a first image of a mouth. A first function is generated for a color channel based on intensities of the color channel at the first set of pixel locations and depth values associated with the first set of pixel locations. Image data comprising a new representation of the teeth is received, wherein the image data comprises a second set of pixel locations and new depth values associated with the second set of pixel locations. A new image is generated based on the image data and the first function, wherein a shape of the teeth is based on the image data and a color of the teeth is based on applying the first function to the second set of pixel locations for the teeth and the new depth values.

Generic framework for blurring of colors for teeth in generated images using height map

Systems and methods of simulating dental treatments are disclosed. A method may include capturing a first 2D image of a patient's face, including the patient's teeth, building a parametric 3D model of the patient's teeth based on the 2D image, developing a simulated outcome of a dental treatment of the patient's teeth by rendering the 3D model with the patient's teeth in one or more positions and/or orientations corresponding to the treatment goals of the dental treatment plan, and rendering a second 2D image of the patient's face with teeth according to a simulated outcome of the dental treatment plan. As noted herein, the dental treatment plan may include orthodontic and/or restorative elements. The simulated outcome may correspond to estimated outcomes and/or intended outcomes of the dental treatment plan.

Providing a simulated outcome of dental treatment on a patient

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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Morrisville, NC, United States of America

Chao Shi