Abstract: Simulating draping of a 3-dimensional (3D) fabric by providing user input representing physical property parameters of a fabric through a graphical user interface. Once the physical property parameters are received, a 3D shape of fabric is generated by applying the physical property parameters to a neural network.

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 tooth segmentation of a three dimensional scan data includes converting the three dimensional scan data into a two dimensional image, determining a three dimensional landmark using a first artificial intelligence neural network receiving the two dimensional image, generating cut data by cutting the scan data using the three dimensional landmark, determining an anchor point using the three dimensional landmark and the cut data, generating a mapped data by mapping the cut data into a predetermined space using the anchor point, determining a segmentation mask using a second artificial intelligence neural network receiving the mapped data and mapping the segmentation mask to the scan data or to the cut 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 classification apparatus includes a 3D (three dimensional) Inception-Resnet block structure, a global average pooling structure and a fully connected layer. The 3D Inception-Resnet block structure includes a 3D Inception-Resnet structure configured to receive 3D medical image of a patient's shoulder and extract features from the 3D medical image and 3D Inception-Downsampling structure configured to downsample information of a feature map including the features. The global average pooling structure is configured to operate an average pooling for an output of the 3D Inception-Resnet block structure. The fully connected layer is disposed after the 3D global average pooling structure. The automated classification apparatus is configured to automatically classify the 3D medical image into a plurality of categories.