A comparator may automatically detect and quantify subtle and/or microscopic variance to a feature of a three-dimensional (“3D”) object in a reproducible manner based on point cloud imaging of that 3D object. The comparator may isolate a first set of data points, that represent the object feature at a first time, in a reference point cloud, and may isolate a second set of data points, that represent the same but altered object feature at a different second time, in a non-reference point cloud. The comparator may detect variance between positional values and visual characteristic values of the second set of data points and the corresponding positional values and visual characteristic values of the first set of data points, and may quantify a change occurring to the object feature between the first time and the second time based on a mapping of the variance to a particular unit of measure.

A method for ascertaining pulmonary fibrosis disease progression or treatment response includes obtaining a first set of computed tomography (CT) images of a lung and determining a first Pulmonary Surface Index (PSI) score for the lung by detecting a first actual lung boundary of the lung within the first set of CT images, determining a first approximated lung boundary within the first set of CT images, and determining the PSI score using inputs based on the first actual lung boundary and the first approximated lung boundary. The method also includes obtaining a second set of CT images of the lung and determining a second PSI score for the lung using inputs based on a second actual lung boundary and a second approximated lung boundary. The method also includes assessing pulmonary fibrosis treatment response or disease progression based on the first PSI score and the second PSI score.

A method including: automatically detecting, using at least one machine learning algorithm, one or more abnormalities depicted in a medical image of a patient; automatically determining whether the one or more abnormalities have remained temporally and unchanged, based on an older medical image of the patient; and upon determining that the one or more abnormalities have remained temporally and spatially unchanged: automatically inpainting the one or more abnormalities in the medical image, and automatically enrich a new training set with the inpainted medical image.

Provided herein are apparatuses (e.g., systems) and methods for assisting in generating and segmenting a 3D dental model of a subject's dentition. A 3D dental model may be generated from a dental scan. The apparatuses described herein can determine if the subject has previously undergone a dental or orthodontic treatment, and the 3D dental model can be compared to prior 3D dental models from the previous treatment(s). In some examples, the 3D dental model can be updated or supplemented with data from the prior 3D dental models.

An approach for training, on a computer, one or more deep learning algorithms with a plurality of mammograms with known outcomes based, at least in part, on using a set of mammograms of each patient in the plurality of mammograms. The approach includes receiving a first set of mammograms of a first patient. The first set of mammograms includes an unevaluated mammogram of the first patient and a set of prior mammograms of the first patient. The approach includes the trained convolutional neural network extracting the set of features from each mammogram of the set of mammograms of the first patient. Furthermore, the approach includes using a second deep learning algorithm of the one or more deep learning algorithms to perform an evaluation of the unevaluated mammogram of the first patient based, at least in part, on the set of prior mammograms of the first patient.

Various computational methods and techniques are presented to increase the lateral and axial resolution of an ultrasound imager in order to allow a medical practitioner to use an ultrasound imager in real time to obtain a 3D map of a portion of a body.

Systems and methods for medical imaging and analysis are described. The systems and methods comprise generating raw medical image data from a medical imaging hardware device, processing the raw medical image data, generating a processed raw medical image file, transmitting the processed medical image data file and imaging data, identifying a normalization factor based on the imaging detail data, normalizing the processed medical image data file using the normalization factor, and comparing the processed medical image data file with at least one other processed medical image data file. The difference between the processed medical image file and the at least one other processed medical image file is subtracted. A graphical representation of the difference is generated and displayed.

In some implementations, a method includes obtaining a computer graphic generated based on one or more visual elements within a first video frame. In some implementations, the first video frame is associated with a first time. In some implementations, the method includes obtaining a second video frame associated with a second time. In some implementations, the second time is different from the first time. In some implementations, the method includes applying an intensity transformation to the computer graphic in order to generate a transformed computer graphic. In some implementations, the intensity transformation is based on an intensity difference between the first video frame and the second video frame. In some implementations, the method includes rendering the transformed computer graphic based on one or more visual elements within the second video frame.

An information processing apparatus includes a first acquisition unit configured to acquire a first medical image of a target object and a second medical image of the target object captured at different time phases from each other, a second acquisition unit configured to acquire first contour information about the target object in the first medical image and second contour information about the target object in the second medical image, a generation unit configured to generate first processed information in which the first contour is blurred and second processed information in which the second contour is blurred, and a third acquisition unit configured to acquire deformation information about the target object between the first medical image and the second medical image based on the first processed information and the second processed information.

An information processing apparatus includes a first acquisition unit configured to acquire a characteristic amount relating to movement of a target site of a subject, a second acquisition unit configured to acquire a standard characteristic amount, based on a characteristic amount relating to movement of a target site of a standard subject different from the subject, and a calculation unit configured to calculate a characteristic value relating to the movement of the target site of the subject, based on the characteristic amount relating to the movement of the target site of the subject and the standard characteristic amount, wherein the second acquisition unit performs a coordinate transformation of the characteristic amount of the standard subject into a reference space, and calculates the standard characteristic amount, based on a characteristic amount resulting from the coordinate transformation.