The present invention relates to an apparatus for predicting a region-specific cerebral cortical contraction rate on the basis of a CT image. The present invention may comprise: a deep learning step of a deep learning network learning, by selecting and using CT images of a plurality of patients and segmentation information thereof, a correlation between the CT images and the segmentation information; a feature extraction step of extracting, on the basis of each piece of the segmentation information, semantic feature information corresponding to the CT images; a machine learning step of a machine learning model learning, after a plurality of region-specific cerebral cortical contraction rates corresponding to each piece of the semantic feature information are additionally acquired, a correlation between the semantic feature information and the region-specific cerebral cortical contraction rates; a segmentation step of, when an image to be analyzed is input, acquiring segmentation information corresponding to the image to be analyzed, through the deep learning network; and a prediction step of predicting and reporting, after semantic feature information corresponding to the image to be analyzed is extracted on the basis of the segmentation information, a region-specific cerebral cortical contraction rate corresponding to the semantic feature information through the machine learning model.

The present disclosure relates to a method, an apparatus, and a system for detecting and classifying an ischemic stroke based on a medical image. A medical image based ischemic stroke detecting and type classifying apparatus according to an aspect of the present disclosure includes an acquiring unit which collects images related to a brain of at least one patient; a detecting unit which determines whether the at least one patient is a large vessel occlusion patient, based on the collected image; a determining unit which determines whether a type of the large vessel occlusion is embolism or intracranial atherosclerosis (ICAS), when the at least one patient is a large vessel occlusion patient; and a diagnosing unit which provides treatment direction information which is applied differently according to the determined type of the large vessel occlusion.

A surgical access assembly and method of use is disclosed. The surgical access assembly comprises an outer sheath and an obturator. The outer sheath and obturator are configured to be delivered to an area of interest within the brain. Either the outer sheath or the obturator may be configured to operate with a navigational system to track the location of either within the brain. Once positioned at a desired location, the obturator is removed, leaving a distal end of the outer sheath adjacent an area of interest, and creating a working corridor. Interrogation of the area of interest may be performed to evaluate a disorder and/or abnormality, as well as evaluate treatment regimes. Interventional devices may also be introduced to the area of interest, as well as a variety of treatments.

A method of diagnosing Alzheimer's disease using a positron emission tomography-computed tomography (PET-CT) image may include generating a standard brain CT template in a Montreal Neurological Institute (MNI) region based on a CT image calculated from a PET-CT apparatus, calculating a whole cortex volume of interest (VOI) for a plurality of detail regions capable of being used in .sup.18F-florbetaben (FBB) and .sup.18F-flutemetamol (FMM) in common within a cortex ROI region in which a deposition of beta amyloid protein is equal to or higher than a given value based on the standard brain CT template, and calculating a centiloid of each of the plurality of detail regions based on an amyloid standardized uptake value ratio (SUVR) of each of the plurality of detail regions.

The present disclosure discloses a three-dimensional automatic location system for an epileptogenic focus based on deep learning. The system includes: a PET image acquisition and labelling module; a registration module mapping PET image to standard symmetrical brain template; a PET image preprocessing module generating mirror image pairs of left and right brain image blocks; a network SiameseNet training module containing two deep residual convolutional neural networks which share weight parameters, an output layer connecting a multilayer perceptron and a softmax layer, and using a training set of an epileptogenic focus image and an normal image to train the network to obtain a network model; a classification module and epileptogenic focus location module, using the trained network model to generate a probabilistic heatmap for the newly input PET image, a classifier determining whether the image is normal or epileptogenic focus sample, and then predicting a position for the epileptogenic focus region.

In one embodiment a medical tracking system, including a catheter to be inserted into blood vessels of a body-part of a living subject, and including a flexible shaft having a deflectable distal end, and a location tracking transducer in the distal end configured to output a signal indicative of a location of the transducer, a tracking subsystem to track locations of the distal end over time responsively to the signal, a display, and processing circuitry to add the tracked locations of the distal end to a movement log, and render to the display an image of at least part of the body-part with a representation of a length of the shaft of the catheter in at least one blood vessel of the body-part, with respective positions along the length of the shaft being located in the image responsively to respective ones of the tracked locations from the movement log.

An electrode module is positioned inside an intracranial portion of a human head. Then, it captures brain images of the human head so multiples two dimensional (2D) cross-sectional images are obtained. The electrodes can be seen in one or more 2D cross-sectional images. A brain functional map adjusting portion is provided to obtain the 2D cross-sectional images and then to conduct a proportional deformation process for the images in the brain functional map database. By combining the processed images in the brain functional map database and the 2D cross-sectional images, multiple combined cross-sectional images can be obtained for display. So, the effects of intracranial electrodes are better than the traditional way. In addition, the brain structure information of a patient contains the precise positions of the electrodes and the corresponding brain functional areas.

The present invention is directed to a miniaturized biosensor and nanotechnology which is embedded in a variety of medical devices which can be used for real-time device location tracking and analysis, for the purpose of optimizing device positioning both at the time of initial placement and throughout its clinical use (i.e., device continuum). The continuously acquired device-specific standardized data is then transmitted through wireless communication networks to provide continuous feedback and alerts to authorized clinical providers as to device positioning, clinical performance, and presence of pathology.

An automated process for data annotation of medical images includes obtaining image data from an imaging sensor, partitioning the image data, identifying an object of interest in the partitioned image data, generating an initial contour with one or more control points with respect to the object of interest, identifying a manual adjustment of one of the control points, automatically adjust a position of at least one other control point within a predetermined range of the manually adjusted control point to a new position, the new position of the at least one other control point and manually adjusted control point defining a new contour, and generating an updated image with the new contour and corresponding control points.

An extra-oral dental imaging apparatus can obtain a radiographic image of a portion of a head of a patient. Exemplary dental apparatus and/or method embodiments can position a subject for dental radiographic imaging by providing a bitable dental arch mounting apparatus to offset the antero-posterior plane of the dental imaging apparatus and the plane of symmetry of the dental arch mounting apparatus. In one embodiment, the offset can be provided by a tilted dental arch mounting apparatus (e.g., relative to the horizontal).