Abstract: An automated method for aligning a 3D single tooth model to 3D oral scan data includes determining an oral scan landmark and a teeth curve formed by teeth in the 3D oral scan data, determining a margin line of a target tooth in the 3D oral scan data and a region of interest in the 3D oral scan data, determining first to third axes of the 3D oral scan data in the region of interest based on the oral scan landmark, determining a single tooth landmark of the 3D single tooth model, determining fourth to sixth axes of the 3D single tooth model based on the single tooth landmark and aligning the 3D single tooth model to the 3D oral scan data such that the fourth to sixth axes of the 3D single tooth model respectively overlap the first to third axes of the 3D oral scan data.

Abstract: A method of automatic segmentation of a maxillofacial bone in a CT image using a deep learning, the method includes receiving input CT slices of the CT image including the maxillofacial bone, segmenting the input CT slices into a mandible and a portion of the maxillofacial bone excluding the mandible using a convolutional neural network structure and accumulating 2D segmentation results which are outputs of the convolutional neural network structure to reconstruct a 3D segmentation result. The convolutional neural network structure includes an encoder including a first operation and a second operation different from the first operation in a same layer and a decoder including a third operation and a fourth operation different from the third operation in a same layer.

Abstract: A prosthesis generating method using three dimensional scan data includes receiving encryption oral data, receiving a model information of an artificial intelligence neural network, determining meta data of the encryption oral data, determining an operation predicted value based on the model information and the meta data and determining a size of a sub-operator based on the operation predicted value. The size of the sub-operator is controlled based on the operation predicted value.

Abstract: A method for deforming a shape of a tooth model includes generating a plurality of deformation shape models by deforming a tooth template model based on tooth scan data and generating a blended shape model by performing mesh blending on the deformation shape models.

Abstract: An automated method for generating a prosthesis from a three dimensional (“3D”) scan data, the method includes generating an intermediate surface of the prosthesis extending toward an outside of a prepared tooth from a margin line of the prepared tooth in the 3D scan data, generating an inner surface of the prosthesis by determining a gap from a surface of the prepared tooth, generating an outer surface of the prosthesis and connecting the outer surface of the prosthesis and the intermediate surface of the prosthesis.

Abstract: An automated method includes detecting a tooth of the scan data using a first artificial intelligence neural network, extracting a tooth scan data from the scan data based on a result of a tooth detection, generating a tooth mapped data corresponding to a predetermined space based on the tooth scan data, generating the tooth boundary curve by inputting the tooth mapped data to a second artificial intelligence neural network and mapping the tooth boundary curve to the scan data.

Abstract: An automated method for an inferior alveolar nerve canal segmentation of three dimensional volume data according to the present inventive concept, the method includes determining a central feature point and an end feature point from the three dimensional volume data including an inferior alveolar nerve canal, separating a central region of the inferior alveolar nerve canal from the three dimensional volume data based on the central feature point, separating an end region of the inferior alveolar nerve canal from the three dimensional volume data based on the end feature point, and reconstructing the inferior alveolar nerve canal based on the central region of the inferior alveolar nerve canal and the end region of the inferior alveolar nerve canal.

Abstract: A method for providing a user interface for manufacturing a prosthesis includes identifying teeth from three-dimensional oral data of a patient, displaying a target tooth for a prosthesis manufacturing among the teeth on a screen in a selectable state by a user, displaying a first wheel menu for generation of the prosthesis for the target tooth for dental work or data removal operation, and displaying a first tool menu providing items for the generation of the prosthesis corresponding to the target tooth for dental work on the screen.

Abstract: A method of representing a dental object using a binary space partitioning for performing a binary space partitioning according to the present inventive concept, the method includes generating internal trees for partitioning a space including the dental object represented as a polygon mesh, and generating leaf trees for representing a shape of the dental object in spaces generated based on the internal trees. Each of the internal trees and the leaf trees includes internal nodes and leaf nodes.

Abstract: An automated method for classifying a prosthesis type from a three dimensional oral data includes aligning a three dimensional oral data including tooth, extracting a feature for determining the prosthesis type to be used for the tooth from the three dimensional oral data which is aligned, combining the three dimensional oral data which is aligned with a feature data including the feature, and classifying the prosthesis type to be used for the tooth based on the three dimensional oral data which is aligned and the feature data.

Abstract: A method of automated tooth segmentation of a three dimensional scan data using a deep learning, includes determining a U-shape of teeth in input scan data and operating a U-shape normalization operation to the input scan data to generate first scan data, operating a teeth and gum normalization operation, in which the first scan data are received and a region of interest (ROI) of the teeth and gum is set based on a landmark formed on the tooth, to generate second scan data, inputting the second scan data to a convolutional neural network to label the teeth and the gum and extracting a boundary between the teeth and the gum using labeled information of the teeth and the gum.

Abstract: An automated registration method of 3D facial scan data and 3D volumetric medical image data using deep learning, includes extracting scan landmarks from the 3D facial scan data using a convolutional neural network, extracting volume landmarks from the 3D volumetric medical image data using the convolutional neural network and operating an initial registration of the 3D facial scan data and the 3D volumetric medical image data using the scan landmarks and the volume landmarks.

Abstract: A method of generating a 3D model for digital dentistry using a virtual bridge based multi input Boolean operation, includes generating a first group model by generating a first virtual bridge connecting models spaced apart from each other among first input models of a first input group when the models are spaced apart from each other among the first input models, generating a second group model by generating a second virtual bridge connecting models spaced apart from each other among second input models of a second input group when the models are spaced apart from each other among the second input models, generating a first result model by a Boolean operation of the first group model and the second group model and removing a remaining first virtual bridge or a remaining second virtual bridge when the first virtual bridge or the second virtual bridge remains in the first result model.

Abstract: An automated method for aligning 3D (three-dimensional) dental data includes extracting landmark points of a CT (computerized tomography) data, extracting landmark points of scan data of a digital impression model, determining an up vector representing a direction of a patient's eyes and nose and identifying left and right of the landmark points of the scan data, extracting a teeth portion of the scan data, searching a source point of the scan data on a spline curve of the CT data to generate a candidate target point group and determining the candidate target point group having a smallest error with the landmark points of the CT data as a final candidate.

Abstract: An automated method for aligning 3D (three-dimensional) dental data includes extracting landmark points of a CT (computerized tomography) data, extracting landmark points of scan data of a digital impression model, determining an up vector representing a direction of a patient's eyes and nose and identifying left and right of the landmark points of the scan data, extracting a teeth portion of the scan data, searching a source point of the scan data on a spline curve of the CT data to generate a candidate target point group and determining the candidate target point group having a smallest error with the landmark points of the CT data as a final candidate.

Abstract: A method for deforming a shape of a tooth model includes generating a plurality of deformation shape models by deforming a tooth template model based on tooth scan data and generating a blended shape model by performing mesh blending on the deformation shape models.

Abstract: An automated method for generating a prosthesis from a three dimensional (“3D”) scan data, the method includes generating an intermediate surface of the prosthesis extending toward an outside of a prepared tooth from a margin line of the prepared tooth in the 3D scan data, generating an inner surface of the prosthesis by determining a gap from a surface of the prepared tooth, generating an outer surface of the prosthesis and connecting the outer surface of the prosthesis and the intermediate surface of the prosthesis.

Abstract: A method for automated detection of landmarks from 3D medical image data using deep learning according to the present inventive concept, the method includes receiving a 3D volume medical image, generating a 2D intensity value projection image based on the 3D volume medical image, automatically detecting an initial anatomical landmark using a first convolutional neural network based on the 2D intensity value projection image, generating a 3D volume area of interest based on the initial anatomical landmark and automatically detecting a detailed anatomical landmark using a second convolutional neural network different from the first convolutional neural network based on the 3D volume area of interest.

Abstract: An automated method for generating a prosthesis from a 3D scan data, the method includes extracting prep information of a prepared tooth from the 3D scan data, generating a two dimensional (“2D”) projection images by projecting the 3D scan data based on the prep information and generating a 3D prosthesis based on the 2D projection images using a generative adversarial network including a 2D encoder and a 3D decoder.

Abstract: An automated method for generating a prosthesis from a 3D scan data, the method includes automatically extracting tooth information of a tooth included in the 3D scan data from the 3D scan data, automatically extracting a margin line of a prepared tooth, generating a plurality of two dimensional (“2D”) images including the prepared tooth and an adjacent tooth adjacent to the prepared tooth, automatically generating a 3D temporary prosthesis data based on the plurality of 2D images and deforming a single tooth model corresponding to the prepared tooth using the margin line and the 3D temporary prosthesis data to generate a 3D prosthesis data.