The present disclosure relates to systems, devices, and methods to augment a two-dimensional image.

Systems and methods for detecting complex networks in MRI image data in accordance with embodiments of the invention are illustrated. One embodiment includes an image processing system, including a processor, a display device connected to the processor, an image capture device connected to the processor, and a memory connected to the processor, the memory containing an image processing application, wherein the image processing application directs the processor to obtain a time-series sequence of image data from the image capture device, identify complex networks within the time-series sequence of image data, and provide the identified complex networks using the display device.

A target location for a therapeutic intervention is determined in a subject with a neurological disorder. The target location is selected within at least one resting state network (RSN) map according to a predetermined criterion for the neurological disorder. The at least one RSN map includes a plurality of functional voxels within a brain of the subject, and each functional voxel of the plurality of functional voxels is associated with a probability of membership in an RSN. Instructions are transmitted to a treatment system that cause operation to be performed on the selected target location.

An automatic clipping technique capable of satisfactorily extracting blood vessels to be extracted is provided. A specific tissue extraction mask image which is created by extracting a specific tissue (for example, a brain) from a three-dimensional image acquired by magnetic resonance angiography and a blood vessel extraction mask image which is created by extracting a blood vessel from an area (a blood vessel search area) which is determined using a preset landmark position and the specific tissue extraction mask image are integrated to create an integrated mask. By applying the integrated mask to the three-dimensional image, a blood vessel is clipped from the three-dimensional image.

A non-transitory computer-readable medium (CRM) storing computer program code executed by a computer processor that executes a process, an information processing apparatus, and a model generation method that outputs complication information for a medical treatment. The process includes acquiring a medical image obtained by imaging a lumen organ of a patient before treatment, inputting the acquired medical image into a trained model so as to output complication information on a complication that is likely to occur after the treatment when the medical image is received, and outputting the complication information. Preferably, complication information including a type of the complication that is likely to occur and a probability value indicating an occurrence probability of the complication of the type is output.

The current disclosure provides methods and systems for visualizing, comparing, and navigating through, labeled image sequences. In one example, a degree of variation between a plurality of labels for an image in a sequence of images may be encoded as a comparison metric, and the comparison metric for each image may be graphed as a function of image position in the sequence of images, thereby providing a contextually rich view of label variation as a function of progression through the sequence of images. Further, the encoded variation of image labels may be used to automatically flag inconsistently labeled images, wherein the flagged images may be highlighted in a graphical user interface presented to a user, pruned from a training dataset, or a loss associated with the flagged image may be scaled based on the encoded variation during training of a machine learning model.

In accordance with some embodiments of the disclosed subject matter, systems, methods, and media for selectively presenting images captured by confocal laser endomicroscopy (CLE) are provided. In some embodiments, a method comprises: receiving images captured by a CLE device during brain surgery; providing the images to a convolution neural network (CNN) trained using at least a plurality of images of brain tissue captured by a CLE device and labeled diagnostic or non-diagnostic; receiving an indication, from the CNN, likelihoods that the images are diagnostic images; determining, based on the likelihoods, which of the images are diagnostic images; and in response to determining that an image is a diagnostic image, causing the image to be presented during the brain surgery.

A method may include obtaining first image data relating to a region of interest (ROI) of a first subject. The first image data corresponding to a first equivalent dose level may be acquired by a first device. The method may also include obtaining a model for denoising relating to the first image data and determining second image data corresponding to an equivalent dose level higher than the first equivalent dose level based on the first image data and the model for denoising. In some embodiments, the method may further include determining information relating to the ROI of the first subject based on the second image data and recording the information relating to the ROI of the first subject.

An endoscopic image observation system supports the observation of a plurality of images captured by a capsule endoscope. The endoscopic image observation system includes a distinguishing unit that outputs an accuracy score indicating the likelihood that each of the plurality of images represents an image of a region sought to be distinguished; a grouping unit that groups the plurality of images into a plurality of clusters in accordance with the accuracy score; and an identification unit that identifies a candidate image for a boundary of the region from among the plurality of images in accordance with the grouping into the plurality of clusters.

A method and system are disclosed for generating a machine learning model for automatic classification of radiographic images acquired by various acquisition protocols. The method includes the steps of: providing a plurality of radiographic images, detecting and segmenting in each of the radiographic image at least one regions of interest (ROI) as reference ROI, measuring at least one radiomic feature per reference ROI, identifying valid reference ROIs based on the measured radiomics values, and clustering the measured radiomics values of valid reference ROIs into at least two reference clusters according to a set of characteristics of image acquisition. A method and system are disclosed for classifying radiographic images by applying a machine learning model generated for automatic classification of radiographic images.