A computer implemented method by which digital images of the human brain can be used to diagnose or to predict cognitive impairment, such as Alzheimer's disease and other forms of cognitive impairment such as so-called prodromal Alzheimer's disease. Methods of classifying or stratifying cohorts of human subjects such as for the purpose of clinical trials and/or to assess the impact of therapies are included. In some embodiments the images comprise T1 weighted MRI images.

The invention relates to systems and methods for predicting ischemic brain tissue fate from multi-phase CT-angiography. More specifically, systems and methods are provided that enable meaningful prediction of core, penumbra and perfusion from mCTA images using software that has been trained via machine learning to interpret mCTA images.

Embodiments disclosed herein generally relate to connected machine learning models with joint training for lesion detection. Particularly, aspects of the present disclosure are directed to accessing a three-dimensional magnetic resonance imaging (MRI) image, wherein the three-dimensional MRI image depicts a region of a brain of a subject, wherein the region of the brain includes at least a first type of lesions and a second type of lesions; inputting the three-dimensional MRI image into a machine-learning model comprising a first convolutional neural network and a second convolutional neural network; generating a first segmentation mask for the first type of lesions using the first convolutional neural network that takes as input the three-dimensional MRI image; generating a second segmentation mask for the second type of lesions using the second convolutional neural network that takes as input the three-dimensional MRI image; and outputting the first segmentation mask and the second segmentation mask.

A virtual satellite system may receive, re-project to a spatial resolution and interpolate to a desired temporal resolution, georeferenced data representing an image of a geographic region from a plurality of different satellites. Bias in the georeferenced data between the plurality of satellites is determined and based on which satellite's image data contains an identified minimum spatial resolution, vegetation index data may be set to one of the satellite's data, which may or may not be adjusted. A target image may be generated based on the set vegetation index data.

A device may obtain field images of a tissue sample, apply, to the field images, spatial distortion and illumination-based corrections (including corrections for photobleaching of reagents) to derive processed field images, identify, in each processed field image, a primary area including data useful for cell or subcellular component characterization, identify, in the processed field images, areas that overlap with one another, and derive information regarding a spatial mapping of cell(s) and/or sub-cellular components of the tissue sample. Deriving the information may include performing segmentation based on the data included in the primary area of each processed field image, and obtaining flux measurements based on other data included in the overlapping areas. The device may cause the information to be loaded in a data structure to enable statistical analysis of the spatial mapping for identifying factors defining normal tissue structure, associated inflammatory or neoplastic diseases and prognoses thereof, and associated therapeutics.

A computer-implemented method is for classifying a lesion. In an embodiment, the method includes receiving a first medical image of an examination volume, the first medical image corresponding to a first examination time; receiving a second medical image of the examination volume, the second medical image corresponding to a second examination time, different from the first examination time; determining a first lesion area corresponding to a lesion within the first medical image; determining a registration function based on a comparison of the first medical image and the second medical image; determining a second lesion area within the second medical image based on the registration function and the first lesion area; and classifying the lesion within the first medical image based on the second lesion area. A computer-implemented method for providing a trained classification function, a classification system, and computer program products and computer-readable media are also disclosed.

A microbubble counting method for patent foramen ovale (PFO) based on deep learning is provided. The method includes: segmenting a target area of a left heart in an ultrasonic image; and generating a corresponding density map for a segmented target image using a convolutional neural network (CNN), and calculating a total number of the microbubbles in the segmented area by integration and summation. The method has the following beneficial effects: target segmentation is performed on the left atrium and left ventricular area of the heart using the neural network, and effective segmentation of the target area of the left heart is the key of obtaining parameters such as a size and form of the target area. The target area is quantitatively analyzed according to a segmentation result, and the number of the microbubbles in the target area is counted.

An image processing apparatus includes: an obtaining unit configured to obtain a first medical image and a second medical image collected from an object; an identification unit configured to identify a plurality of sites of the object included in the first medical image; and a subtraction image generation unit configured to generate a subtraction image between the first medical image and the second medical image by calculating, for each of a plurality of pixels forming the first medical image, a subtraction value with respect to a pixel, corresponding to the pixel, on the second medical image by a subtraction generation method corresponding to a site based on the result of the identification unit.

Provided herein are methods and systems to train and execute a motion model that uses artificial intelligence methodologies (e.g., deep-learning) to learn and predict location of a patient's internal structures. A method comprises receiving respiratory data of a patient from an electronic sensor in addition to a medical image, such as kV image; executing an artificial intelligence model using the respiratory data and predicting deformation data for at least one internal structure of the patient, wherein the artificial intelligence model is trained in accordance with a training dataset comprising a set of participants, their corresponding respiratory data, and their corresponding deformation data; and outputting the predicted deformation data.

A pre-event connectome of a subject brain is accessed, the pre-event connectome defining i) first functional nodes in the subject brain and ii) first edges that represent connections between the first functional nodes before the subject has undergone an event. A post-event connectome of the subject brain is accessed, the post-event connectome defining i) second functional nodes in the subject brain and ii) second edges that represent connections between the second functional nodes after the subject has undergone the event. A connectome-difference map data is generated that records the difference between the pre-event connectome and the post-event connectome. An action is taken based on the connectome-difference map data.