A dynamic anatomic atlas is disclosed, comprising static atlas data describing atlas segments and dynamic atlas data comprising information on a dynamic property which information is respectively linked to the atlas segments.

A method of determining the position and orientation of an invasive medical device is described using altered devices and a machine learning algorithm for detecting the position and orientation of such devices from imagery. Such predictions can subsequently be displayed to an operator to improve the speed and accuracy by which they perform procedures.

A method for calculating the risk of aortic rupture or dissection of an individual with an ascending thoracic aortic aneurysm, ATAA, is disclosed. The method includes the steps of obtaining a first data set linked to the clinical and/or demographic characteristics of the individual, obtaining a second data set linked to the biochemical characteristics of a biological sample of the individual, obtaining a third data set linked to the morphological and functional characteristics of the aorta and processing the third data set to obtain a fourth data set by computational modelling, integrating the first data set, the second data set, the third data set and the fourth data set in a predictive model to obtain a risk index (i) of aortic rupture or dissection, wherein the second data set includes expression values of at least one biomarker.

A dynamic analysis system includes a hardware processor and an output device. The hardware processor obtains a cycle of temporal change in a feature amount relevant to a function to be diagnosed from each of dynamic images obtained by imaging of a dynamic state of a living body with radiation. The hardware processor further adjusts the obtained cycle, thereby generating a plurality of cycle-adjusted data having cycles of the temporal change in the feature amount being equal to one another. The hardware processor further generates difference information at each phase in the plurality of cycle-adjusted data. The output device outputs the difference information.

A medical scan annotator system is operable to select a medical scan for transmission via a network to a first client device and a second client device for display via an interactive interface, and annotation data is received from the first client device and the second client device in response. Annotation similarity data is generated by comparing the first annotation data to the second annotation data, and consensus annotation data is generated based on the first annotation data and the second annotation data in response to the annotation similarity data indicating that the difference between the first annotation data and the second annotation data compares favorably to an annotation discrepancy threshold. The consensus annotation data is mapped to the medical scan in a medical scan database.

A computer-implemented method is for evaluating a condition of a person. The method includes determining at least one characteristic of a first facial expression of at least a mouth of the person, at a first time, based at least on a first image previously captured; determining at least one characteristic of a second facial expression of at least a mouth of a person, at a second time, based at least on a second image previously captured, the first facial expression and the second facial expression being of a same first type of facial expression; determining at least one difference between the at least one characteristic of the first facial expression determined and the at least one characteristic of the second facial expression determined; and generating an output signal indicating the condition of the person based at least on the at least one difference determined.

A division unit 22 divides a brain included in a first brain image into a plurality of regions by performing registration between the first brain image including a brain of a subject and a standard brain image divided into a plurality of regions. A registration unit 23 performs registration between the first brain image and a second brain image that includes the brain of the subject and has a different imaging date and time from the first brain image. A change amount acquisition unit 24 acquires the amount of change from a corresponding region in the brain included in the first brain image, for at least one region of the plurality of regions in the brain included in the second brain image, based on the registration result.

A method for precision cancer treatment by identifying drug resistance is provided. In some embodiments, the method may include: detecting tumor oxygenated perfusion by having a patient breathe air to acquire MRI baseline data; inhalation of hyperoxia gas to generate higher than baseline HbO.sub.2 blood circulating in body to acquire MRI enhanced data; the region-of-interest (ROI), which in this case is a tumor volume (V.sub.0), and which may be performed by volume contour tracing/region-of-interest (ROI) analysis and 3D tumor volumetry methods; calculating voxel's enhanced signal intensity (SI); calculating tumor oxygenated perfusion percentage (OPP %); selecting different threshold and calculating maps such as a reconstruction OPP % pseudo color map; calculating tumor volume change ratio (Vt %); overlaying reconstruction OPP % pseudo color map to original images for visualizing tumor response data; drawing or plotting the OPP % and Vt % on a cancer treatment response information diagram, and identifying the type of drug resistance, classifying the drug resistance being caused by poor drug distribution factor or cells-specific factor based on pooled collected data.

The present invention provides methods, systems and equipment for acquiring and extracting information using a biological feature. The method for acquiring information using a biological featureincludes: acquiring first data by a terminal; controlling, by the terminal, a light source to generate an intensity-variable light signal according to the first data, and projecting the intensity-variable light signal on an iris; acquiring, by the terminal, first iris information at least comprising first iris contraction information, in which the first iris contraction information is generated by the iris according to the intensity-variable light signal and comprises a first iris contraction activity; acquiring, by the terminal, second iris information at least comprising second iris contraction information; and extracting, by the terminal, second data from the second iris contraction information according to a preset rule.

A method for performing 2D/3D registration includes acquiring a 3D image. A pre-contrast 2D image is acquired. A sequence of post-contrast 2D images is acquired. A 2D image is acquired from a second view. The first view pre-contrast 2D image is subtracted from each of the first view post-contrast 2D images to produce a set of subtraction images. An MO image is generated from the subtraction images. A 2D/3D registration result is generated by optimizing a measure of similarity between a first synthetic 2D image and the MO image and a measure of similarity between a second synthetic image and the intra-operative 2D image from the second view by iteratively adjusting an approximation of the pose of the patient in the synthetic images and iterating the synthetic images using the adjusted approximation of the pose.