Abstract:
A first 3D image and a second 3D image imaged a target organ in different phases of respiration are acquired. A 3D deformation model of the target organ which is stored in advance and represents nonlinear 3D deformation of the target organ due to respiration, and which has been generated based on information about movement of the target organ due to respiration of plural patients, is read. The positions of pixels on the second 3D image representing the same positions on the target organ as plural sampled pixels in a target organ region on the first 3D image are estimated using displacement due to changes in phase of points on the 3D deformation model corresponding to the positions on the target organ represented by the pixels. Non-rigid alignment is performed between the first 3D image and the second 3D image using the estimated positions of the pixels.
Abstract:
A first image and a second image are obtained; the amount of deformation of the first image is estimated by evaluating the degree of similarity between a deformed first image and the second image, using an evaluation function that evaluates the correlation between the distribution of corresponding pixel values within the two images; and an image, which is the first image deformed based on the estimated amount of deformation, is generated. The evaluation function evaluates the degree of similarity between the deformed first image and the second image, based on degrees of similarities of divided images that represent degrees of similarities among the distributions of pixel values of each pair of divided first images and divided second images, which respectively are images that the deformed first image is divided into and images that the second image is divided into, according to predetermined dividing conditions.
Abstract:
There is provided a medical information acquisition device including an information acquisition unit that acquires functional change information obtained on the basis of a reference image and a past image acquired by capturing images of the same subject at a reference time and a past time closer to the past than the reference time, respectively, using a trained model.
Abstract:
A medical image processing apparatus having a processor configured to detect at least four reference landmarks among the left eye, the right eye, the diencephalon, the fornix, the corpus callosum, the left hippocampus, and the right hippocampus from a brain image, performs first registration including registration by similarity transformation using reference landmarks between the brain image and a standard brain image, and perform second registration by nonlinear transformation between the brain image and the standard brain image after the first registration.
Abstract:
The medical information display apparatus includes an image acquisition unit that acquires a first brain image which is a brain image of a subject captured at a first time point and a second brain image captured at a second time point later in time than the first time point, a registration unit that performs registration between the first and the second brain image, an image analysis unit that extracts a first cavity region and a second cavity region from the first brain image and the second brain image, respectively, a display unit, and a display controller displays, a cavity expansion part included in a first non-cavity region, which is a region other than the first cavity region, and included in the second cavity region in a distinguishable manner from a region other than the cavity expansion part from a result of the registration between the first and second brain image.
Abstract:
A medical image display apparatus includes an image acquisition unit that receives an input of a three-dimensional brain image of a subject, a brain area division unit that divides the three-dimensional brain image of the subject into a plurality of brain areas, an image analysis unit that calculates an analysis value for each brain area from the three-dimensional brain image of the subject, a data acquisition unit that acquires information indicating a correspondence between the brain area and a function of the brain, a display unit, and a display controller that displays an image showing the brain image of the subject divided into the brain areas, a function of the brain corresponding to each of the brain areas, and the analysis value on the display unit in association with each other.
Abstract:
A medical image processing apparatus, having a processor configured to divide brains included in a brain image and a standard brain image into a plurality of regions corresponding to each other, calculate a first correction amount between the pixel value of a first reference pixel included in each of the plurality of region in the brain image and the pixel value of a second reference pixel and a second correction amount for matching first other pixel values other than the first reference pixel included in each of the plurality of regions in the brain image with pixel values of second other pixels, and correct the brain image based on the first correction amount and the second correction amount.
Abstract:
An image obtaining unit obtains actual endoscope images, and a virtual endoscope image generating unit generates virtual endoscope images including a plurality of virtual endoscope branch images. A corresponding virtual endoscope image determining unit obtains a plurality of actual endoscope images which were obtained within a predetermined amount of time before the endoscope reached its current position, compares the plurality of actual endoscope images and the plurality of virtual endoscope branch images, and determines a corresponding virtual endoscope image that corresponds to the branch structure closest to the current position of the endoscope, through which the endoscope has passed. A matching unit performs matching between each of a plurality of actual endoscope path images and a plurality of virtual endoscope path images for each of a plurality of paths. A position identifying unit identifies the current position of a leading end of the endoscope based on the results of matching.
Abstract:
A first image and a second image obtained by imaging the same subject with different types of modalities are obtained. The first image is deformed, and similarity between the deformed first image and the second image is evaluated by an evaluation function that evaluates correlation between distributions of corresponding pixel values of the two images to estimate an image deformation amount of the first image. Based on the estimated image deformation amount, a deformed image of the first image is generated. The evaluation function includes a term representing a measure of correlation between a pixel value of the deformed first image and a corresponding pixel value of the second image, wherein the term evaluates the correlation based on probability information that indicates a probability of each combination of corresponding pixel values of the first image and the second image.
Abstract:
Provided are an information processing device, a program, a trained model, a diagnostic support device, a learning device, and a prediction model generation method that can perform prediction with high accuracy using images. An information processing device includes: an information acquisition unit that receives an input of image data and non-image data related to a target matter; and a prediction unit that predicts an aspect related to the matter at a time different from a time when the image data is captured on the basis of the image data and the non-image data input through the information acquisition unit. The prediction unit performs weighting calculation by a calculation method, which outputs a combination of products of elements of a first feature amount calculated from the image data and a second feature amount calculated from the non-image data, to calculate a third feature amount in which the first feature amount and the second feature amount are fused and performs the prediction on the basis of the third feature amount.