Systems and computer-implemented methods of performing image registration. One method includes receiving a first image and a second image acquired from a patient at different times and, in each of the first image and the second image, detecting an upper boundary of an imaged object in an image coordinate system and detecting a lower boundary of the imaged object in the image coordinate system. The method further includes, based on the upper boundary and the lower boundary of each of the first image and the second image, cropping and padding at least one of the first image and the second image to create an aligned first image and an aligned second image and executing a registration model on the aligned first image and the aligned second image to compute a deformation field between the aligned first image and the aligned second image.

According to an embodiment, a method for generating a digital data set for fabricating a physical dental bleaching tray useable to deliver an bleaching agent is disclosed. The method includes obtaining a three-dimensional digital representation of a patient’s dentition including teeth and gingiva; segmenting two or more teeth into individual tooth; identifying a facial surface of at least one of the segmented tooth; defining a facial surface portion including a surface area that is at least partly bound by a virtual boundary that is non- interfacing with the gingiva; generating a modified three-dimensional digital representation using the defined facial surface portion; and generating, based on the modified three-dimensional digital representation, the digital data set configured to be used in fabricating the physical dental bleaching tray.

A computer can operated, including detecting defects, or other physical features, of artificial objects. Image data is received of one or more artificial objects, and applying an image segmentation process to the image data to detect predetermined defects of the one or more artificial objects. The image segmentation process identifies one or more regions of the image data determined to have a likelihood of showing one or more of the predetermined defects. The identified one or more regions is output. The image segmentation process determines severity metrics for the defects in the one or more regions, wherein a severity metric represents a severity or significance of a defect. The image segmentation process further determines a confidence factor for each region of the one or more regions, wherein the confidence factor represents a likelihood of the presence of a predetermined defect in the region.

A method of detecting defects in a composite layup includes capturing, using an infrared camera, reference images of a reference layup being laid up by a reference layup head. The method also includes manually reviewing the reference images for defects, and generating reference defect masks indicating defects in the reference images. The method further includes training, using the reference images and reference defect masks, a neural network, creating a machine learning model that, given a production image as input, outputs a production defect mask indicating the defect location and the defect type of each defect. The method also includes capturing, using an infrared camera, production images of a production layup being laid up by the production layup head, and applying the model to the production images to automatically generate a production defect masks indicating each defect in the production images.

A system and method for controlling characteristics of collected image data are disclosed. The system and method include performing pre-processing of an image using GPUs, configuring an optic based on the pre-processing, the configuring being designed to account for features of the pre-processed image, acquiring an image using the configured optic, processing the acquired image using GPUs, and determining if the processed acquired image accounts for feature of the pre-processed image, and the determination is affirmative, outputting the image, wherein if the determination is negative repeating the configuring of the optic and re-acquiring the image.

A system for performing adaptive focusing of a microscopy device comprises a microscopy device configured to acquire microscopy images depicting cells and one or more processors executing instructions for performing a method that includes extracting pixels from the microscopy images. Each set of pixels corresponds to an independent cell. The method further includes using a trained classifier to assign one of a plurality of image quality labels to each set of pixels indicating the degree to which the independent cell is in focus. If the image quality labels corresponding to the sets of pixels indicate that the cells are out of focus, a focal length adjustment for adjusting focus of the microscopy device is determined using a trained machine learning model. Then, executable instructions are sent to the microscopy device to perform the focal length adjustment.

The album creation application of the present disclosure displays image data, to which trimming is performed, and a template, which includes a slot in which the image data is arranged, so that a slot and image data to be arranged in the slot are selected by use of an input device. Position information of a point of interest in the image to be arranged in the slot is obtained. Composition patterns applicable to the image with designation of the point of interest are presented to the user, and the composition pattern to be applied, which is selected by the user from among the presented composition patterns, is obtained. Trimming is performed based on the point of interest and the selected composition pattern selected. The trimmed images are listed, so that multiple trimmed images are presented to the user as trimming proposals.

The invention relates to a computer-implemented method for automatically detecting anatomical structures (3) in a medical image (1) of a subject, the method comprising applying an object detector function (4) to the medical image, wherein the object detector function performs the steps of: (A) applying a first neural network (40) to the medical image, wherein the first neural network is trained to detect a first plurality of classes of larger-sized anatomical structures (3a), thereby generating as output the coordinates of at least one first bounding box (51) and the confidence score of it containing a larger-sized anatomical structure; (B) cropping (42) the medical image to the first bounding box, thereby generating a cropped image (11) containing the image content within the first bounding box (51); and (C) applying a second neural network (44) to the cropped medical image, wherein the second neural network is trained to detect at least one second class of smaller-sized anatomical structures (3b), thereby generating as output the coordinates of at least one second bounding box (54) and the confidence score of it containing a smaller-sized anatomical structure.

The invention relates to a computing system and a computer-implemented method for classifying images of retina of eyes of subjects. A captured image of a retina is processed to obtain a plurality of different segmented images each having different selected portions of the captured image using different selection rules. The multiple segmented images are provided to respective dedicated machine learning models to output an image classification based on the respective segmented images provided as input. An ensemble classification is determined based on the multiple classifications obtained by means of the multiple trained machine learning models.

A microscopy includes multiple cameras working together to capture image data of a sample having a group of organisms distributed over a wide area, under the influence of an excitation instrument. A first processor is coupled to each camera to process the image data captured by the camera. Outputs from the multiple first processors are aggregated and streamed serially to a second processor for tracking the organisms. The presence of the multiple cameras capturing images from the sample, configured with 50% or more overlap, can allow 3D tracking of the organisms through photogrammetry.