Abstract: Provided are methods for efficient vehicle extent estimation, which can include bounding box generation. Some methods described include determining bounding boxes surrounding detected point clusters according to tangents to convex hulls of the point clusters, and minimizing continuous functions of distances between points and bounding box sides. Accordingly, best-fit bounding boxes are determined more efficiently and quickly, as well as more accurately. Systems and computer program products are also provided.

Abstract: A system for generating a depth output for an image is described. The system receives input images that depict the same scene, each input image including one or more potential objects. The system generates, for each input image, a respective background image and processes the background images to generate a camera motion output that characterizes the motion of the camera between the input images. For each potential object, the system generates a respective object motion output for the potential object based on the input images and the camera motion output. The system processes a particular input image of the input images using a depth prediction neural network (NN) to generate a depth output for the particular input image, and updates the current values of parameters of the depth prediction NN based on the particular depth output, the camera motion output, and the object motion outputs for the potential objects.

Abstract: A deep learning-based MRI examination and diagnosis system for displacement of a temporomandibular joint disc includes: an acquisition unit, configured to acquire a human head MRI image; a labeling unit, configured to obtain temporomandibular joint area images based on the human head MRI image, and label the temporomandibular joint area images to obtain a training set, the temporomandibular joint area images being classified into images in which a joint disc is in normal and abnormal positions in open and closed states; and a diagnosis unit, configured to construct a first temporomandibular joint disc displacement diagnosis model, and train a first jaw joint disc displacement classification model based on the training set to obtain a second temporomandibular joint disc displacement diagnosis model which is configured to classify and recognize temporomandibular joint area images to be diagnosed, and output a temporomandibular joint disc displacement examination and diagnosis result.

Abstract: For creation of a movement-compensated image reconstruction in an x-ray based imaging method an optimal object movement trajectory is determined by application of a trained algorithm to an optimal latent vector and the movement-compensated image reconstruction is created depending on projection images, with the assumption that the object would have moved in accordance with the optimal object movement trajectory while the imaging method was being carried out.

Abstract: Disclosed and described herein are systems and methods of performing computer-aided detection (CAD)/diagnosis (CADx) in medical images and comparing the results of the comparison. Such detection can be used for treatment plans and verification of claims produced by healthcare providers, for the purpose of identifying discrepancies between the two. In particular, embodiments disclosed herein are applied to identifying dental caries (“caries”) in radiographs and comparing them against progress notes, treatment plans, and insurance claims.

Abstract: A method for registering a two-dimensional image data set with a three-dimensional image data set of a body of interest is discloses herein. The method includes the following steps: defining a first vector of a registered virtual camera in a coordinate system of the three-dimensional image data et; obtaining a first transformed vector of an unregistered virtual camera in the coordinate system of the three-dimensional image data set by transforming the first vector of the registered virtual camera through at least one transforming matrix; defining a focal point of the unregistered virtual camera at a reference point of the two-dimensional image data set in the coordinate system of the three-dimensional image data set; and repositioning the unregistered virtual camera according to the first transformed vector and the focal point of the unregistered virtual camera.

Abstract: Disclosed systems and methods relate to remote sensing, deep learning, and object detection. Some embodiments relate to machine learning for object detection, which includes, for example, identifying a class of pixel in a target image and generating a label image based on a parameter set. Other embodiments relate to machine learning for geometry extraction, which includes, for example, determining heights of one or more regions in a target image and determining a geometric object property in a target image. Yet other embodiments relate to machine learning for alignment, which includes, for example, aligning images via direct or indirect estimation of transformation parameters.

Abstract: A computer-implemented method and system of digitally segmenting teeth in a digital model comprises generating a panoramic image from a 3D digital model of a patient's dentition, labeling, using a first trained neural network, the panoramic image to provide a labeled panoramic image, mapping the labeled panoramic image to corresponding coarse digital surface triangle labels in the 3D digital model to provide a labeled 3D digital model, and segmenting the labeled 3D digital model to provide a segmented 3D digital model. A computer-implemented method and system of generating a panoramic image comprises determining, using a trained neural network, digital tooth bounding region(s) corresponding to digital teeth from a 2D depth map of a patient's dentition, connecting digital tooth bounding region(s) by a spline, determining sampled digital surface points from the sampled spline points; and determining associated digital surface points corresponding to each sampled digital surface point.

Abstract: Systems and methods for analyzing pathologies utilizing quantitative imaging are presented herein. Advantageously, the systems and methods of the present disclosure utilize a hierarchical analytics framework that identifies and quantify biological properties/analytes from imaging data and then identifies and characterizes one or more pathologies based on the quantified biological properties/analytes. This hierarchical approach of using imaging to examine underlying biology as an intermediary to assessing pathology provides many analytic and processing advantages over systems and methods that are configured to directly determine and characterize pathology from underlying imaging data.

Abstract: Systems and methods for analyzing pathologies utilizing quantitative imaging are presented herein. Advantageously, the systems and methods of the present disclosure utilize a hierarchical analytics framework that identifies and quantify biological properties/analytes from imaging data and then identifies and characterizes one or more pathologies based on the quantified biological properties/analytes. This hierarchical approach of using imaging to examine underlying biology as an intermediary to assessing pathology provides many analytic and processing advantages over systems and methods that are configured to directly determine and characterize pathology from underlying imaging data.

Abstract: A method for automated segmentation of volumetric images comprising the steps of: receiving a coarse volumetric image comprising a jaw/tooth structure in terms of voxels; defining each voxel a distinct anatomical identifier based on a probabilistic distribution for each of a tooth/non-tooth class; applying the defined coarse image through a coarse model for a coarse output; combining coarse output with the coarse volumetric image; and generating a probabilistic segmentation from applying the combined coarse output/image through a fine model; and converting the probabilistic to a polygonal mesh for each defined class applying a volume-to-mesh algorithm.

Abstract: A method for interpreting an input image by a computing device operated by at least one processor is provided. The method for interpreting an input image comprises storing an artificial intelligent (AI) model that is trained to classify a lesion detected in the input image as suspicious or non-suspicious and, under a condition of being suspicious, to classify the lesion detected in the input image as malignant or benign-hard representing that the lesion is suspicious but determined to be benign, receiving an analysis target image, by using the AI model, obtaining a classification class of a target lesion detected in the analysis target image and, when the classification class is the suspicious, obtaining at least one of a probability of being suspicious, a probability of being benign-hard, and a probability of malignant for the target lesion, and outputting an interpretation result including at least one probability obtained for the target lesion.

Abstract: In various example embodiments, techniques are provided for training and/or using a semantic deep learning model, such as a segmentation-enabled CNN model, to detect corrosion and enable its quantitative evaluation. An application may include a training dataset generation tool capable of semi-automatic generation of a training dataset that includes images with labeled corrosion segments. The application may use the labeled training dataset to train a semantic deep learning model to detect and segment corrosion in images of an input dataset at the pixel-level. The application may apply an input dataset to the trained semantic deep learning model to produce a semantically segmented output dataset that includes labeled corrosion segments. The application may include an evaluation tool that quantitatively evaluates corrosion in the semantically segmented output dataset, to allow severity of the corrosion to be classified.

Abstract: Disclosed are methods and digital tools for deriving tooth condition information for a patient's teeth, for populating a digital dental chart with derived tooth condition information, and for generating an electronic data record containing such information.

Abstract: A method (100) for generating a textual description of a medical image, comprising: receiving (130) a medical image of an anatomical region, the image comprising one or more abnormalities; segmenting (140) the anatomical region in the received medical image from a remainder of the image; identifying (150) at least one of the one or more abnormalities in the segmented anatomical region; extracting (160) one or more features from the identified abnormality; generating (170), using the extracted features and a trained text generation model, a textual description of the identified abnormality; and reporting (180), via a user interface of the system, the generated textual description of the identified abnormality.

Abstract: The present disclosure relates to a video highlight extraction method and system, and a storage medium. The method includes: obtaining a to-be-processed online class video and a teacher-student interaction feature and dividing the to-be-processed online class video into a plurality of target videos; respectively analysis on pictures corresponding to all frames of a target video, to obtain a visual feature set of a student and a visual feature set of a teacher in the pictures corresponding to the frames; determining timeliness of student feedback; performing speech recognition on the speech segment corresponding to the student and the speech segment corresponding to the teacher and extracting a key word, to determine fluency of language of the teacher, fluency of language of the student, and correctness of teaching knowledge; and determining a highlight in the to-be-processed online class video according to priorities of the target videos.

Abstract: A method and system for dimension estimation based on duplication identification is disclosed. In some embodiments, the method includes receiving a set of images of an object. The method includes detecting, from each image in the set of images, a respective image segmentation representing a damage of the object. The method then includes determining a respective dimension for the damage represented by each of the image segmentations. The method further includes determining whether two or more of the image segmentations represent a same damage of the object. Responsive to two or more of the image segmentations representing a same damage of the object, the method includes combining the respective dimensions determined for the damage represented by the two or more image segmentations to obtain a final dimension for the same damage.

Abstract: A computational systems pathology spatial analysis platform includes: (i) a spatial heterogeneity quantification component configured for generating a global quantification of spatial heterogeneity among cells of varying phenotypes in multi-parameter cellular and subcellular imaging data; (ii) a microdomain identification component configured for identifying a plurality of microdomains for tissue samples based on the global quantification, each microdomain being associated with a tissue sample; and (iii) a weighted graph component configured for constructing a weighted graph for the multi-parameter cellular and subcellular imaging data, the weighted graph having a plurality of nodes and a plurality of edges each being located between a pair of the nodes, wherein in the weighted graph each node is a particular one of the microdomains and the edge between each pair of microdomains in the weighted graph is indicative of a degree of similarity between the pair of the microdomains.

Abstract: A device (1) for optical inspection of parisons (2) comprises: an illuminator (3) configured to emit a beam of light directed at a parison (2) located at an inspection position (10); a detector (4) configured to capture an image (10) of the parison (2) interposed between the illuminator (3) and the detector (4), where the illuminator (3) includes an emission-polarizing filter (32) configured to generate a polarized light beam, and where the detector (4) includes a receiving polarizing filter (41) configured to receive the polarized light beam.

Abstract: Provided herein are methods of generating models to predict prospective pathology scores of test subjects having a pathology in certain embodiments. Related systems and computer program products are also provided.