Abstract: Methods for automatically segmenting a 3D model of a patient's teeth may include scanning a patient's dentition and converting the scan data into a 3D model, including a sparse voxel representation of the 3D model. Features can be extracted from the sparse voxel representation of the 3D model and input into a machine learning model to train the machine learning model to segment the 3D model into individual dental components.

Abstract: A method includes receiving an image of a face, processing the image using a first trained machine learning model to determine a bounding shape around teeth in the image, cropping the image based on the bounding shape to produce a cropped image, processing the cropped image using an edge detection operation to generate edge data for the cropped image, and processing the cropped image and the edge data using a second trained machine learning model to label edges in the cropped image.

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.

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.

Abstract: Provided herein are systems and methods for automatically segmenting a 3D model of a patient's teeth. A patient's dentition may be scanned. The scan data may be converted into a 3D model, including a sparse voxel representation of the 3D model. Features can be extracted from the sparse voxel representation of the 3D model and input into a machine learning model to train the machine learning model to segment the 3D model into individual dental components.

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.

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.