Examples relate to systems, methods and computer programs for a microscope system and for determining a transformation function, and to a corresponding microscope system. The system for the microscope system comprises one or more processors and one or more storage devices. The system is configured to obtain first imaging sensor data from a first imaging sensor of a microscope of the microscope system and second imaging sensor data from a second imaging sensor of the microscope, the first imaging sensor data comprises sensor data on light sensed in a first plurality of mutually separated wavelength bands. The second imaging sensor data comprises sensor data on light sensed in a second plurality of mutually separated wavelength bands. The wavelength bands of the first plurality of mutually separated wavelength bands or of the second plurality of mutually separated wavelength bands are wavelength bands that are used for fluorescence imaging. The system is configured to generate a composite color image based on the first imaging sensor data and based on the second imaging sensor data. The composite color image is based on a plurality of color channels. The composite color image is generated using a transformation function to define a transformation to be performed between the imaging sensor data and the composite color image, such that the composite color image is generated using sensor data on light sensed in each wavelength band of the first and second plurality of mutually separated wavelength bands.

The invention relates to a system and method for quantifying a novel biomarker of the tissue activity of a human or animal organ. By way of preferred application, such a biomarker describes the diffusivity of biological fluids in living tissues in the form of a novel indicator of the diffusion of water molecules in living tissues on the basis of diffusion data resulting from the acquisition of a sequence of images of one or more parts of the body of an animal or human patient. Particularly resistant and stable with respect to noise present in the medical imaging signals from which the experimental data stem, the novel biomarker is relevant in a large number of applications including, inexhaustively, the analysis and/or monitoring of cancers, or the assessment of strokes.

Provided herein are systems and methods for automated identification of volumes of interest in volumetric brain images using artificial intelligence (AI) enhanced imaging to diagnose and treat acute stroke. The methods can include receiving image data of a brain having header data and voxel values that represent an interruption in blood supply of the brain when imaged, extracting the header data from the image data, populating an array of cells with the voxel values, applying a segmenting analysis to the array to generate a segmented array, applying a morphological neighborhood analysis to the segmented array to generate a features relationship array, where the features relationship array includes features of interest in the brain indicative of stroke, identifying three-dimensional (3D) connected volumes of interest in the features relationship array, and generating output, for display at a user device, indicating the identified 3D volumes of interest.

Methods, systems, and apparatus for signal artifact detection and reduction are provided. The signal artifact may comprise an interference between an electroencephalography (EEG) signal and a magnetic resonance imaging (MRI) signal arising out of simultaneous EEG and MRI treatment.

An analysis result acquisition unit acquires an analysis result indicating a certainty factor indicating that an abnormality is included in a medical image by analyzing the medical image. A priority deriving unit derives a higher priority as the certainty factor becomes closer to a median value between a maximum value and a minimum value of the certainty factor.

Systems and methods for mapping neuronal circuitry in accordance with embodiments of the invention are illustrated. One embodiment includes a method for generating a neuronal shape graph, including obtaining functional brain imaging data from an imaging device, where the functional brain imaging data includes a time-series of voxels describing neuronal activation over time in a patient's brain, lowering the dimensionality of the functional brain imaging data to a set of points, where each point represents the brain state at a particular time in the timeseries, binning the points into a plurality of bins, clustering the binned points, and generating a shape graph from the clustered points, where nodes in the shape graph represent a brain state and edges between the nodes represent transitions between brain states.

A system for displaying medical imaging data comprising one or more data inputs, one or more processors, and one or more displays, wherein the one or more data inputs are configured for receiving first image data generated by a first medical imaging device, wherein the first image data comprises a field of view (FOV) portion and a non-FOV portion, and the one or more processors are configured for identifying the non-FOV portion of the first image data and generating cropped first image data by removing at least a portion of the non-FOV portion of the first image data, and transmitting the cropped first image data for display in a first portion of the display and additional information for display in a second portion of the display.

A method includes generating first contrast significance data for a first computer vision model generated from a first training set of medical scans. First significant contrast parameters are identified based on the first contrast significance data. A first re-contrasted training set is generated based on performing a first intensity transformation function on the first training set of medical scans, where the first intensity transformation function utilizes the first significant contrast parameters. A first re-trained model is generated from the first re-contrasted training set, which is associated with corresponding output labels based on abnormality data for the first training set of medical scans. Re-contrasted image data of a new medical scan is generated based on performing the first intensity transformation function. Inference data indicating at least one abnormality detected in the new medical scan is generated based on utilizing the first re-trained model on the re-contrasted image data.

A surgical guidance system has two cameras to provide stereo image stream of a surgical field; and a stereo viewer. The system has a 3D surface extraction module that generates a first 3D model of the surgical field from the stereo image streams; a registration module for co-registering annotating data with the first 3D model; and a stereo image enhancer for graphically overlaying at least part of the annotating data onto the stereo image stream to form an enhanced stereo image stream for display, where the enhanced stereo stream enhances a surgeon's perception of the surgical field. The registration module has an alignment refiner to adjust registration of the annotating data with the 3D model based upon matching of features within the 3D model and features within the annotating data; and in an embodiment, a deformation modeler to deform the annotating data based upon a determined tissue deformation.

An apparatus for image segmentation according to an embodiment includes an acquirer configured to acquire one or more images in which an object is photographed and a segmentation performer configured to perform segmentation on the one or more images using a segmentation model which is deep learned through a plurality of images, in which the segmentation model is a U-Net-based model including a first type module based on depth-wise separable convolution (DSC) and a second type module based on global context network (GCNet).