An image processing apparatus that generates a three-dimensional image as a difference image from a first medical image and a second medical image which are three-dimensional images obtained by imaging a subject, the image processing apparatus includes an acquisition unit configured to acquire the first medical image and the second medical image, a determination unit configured to determine a resolution of the difference image, and, a difference image generation unit configured to generate the difference image having the resolution determined by the determination unit, wherein the determination unit determines a resolution of at least one axial direction among three axial directions configuring the resolution of the difference image, based on a resolution of the first medical image and a predetermined first resolution.

A method, an apparatus, and a computer program for determining a refractive error of an eye of a user are provided. The method for determining the refractive error of the eye of the user, wherein the eye of the user has a choroid, includes: ascertaining at least one value for a layer thickness of the choroid of the eye of the user over at least one region of the choroid; and determining a value for the change in the refractive error of the eye only from at least two values for the layer thickness of the choroid which were each ascertained at different times for the at least one region of the choroid, wherein the at least one region is selected from a nasal perifoveal region or a nasal parafoveal region.

A platform is provided for generating 3D models of a tumor segmented from a series of 2D medical images and for identifying from these 3D models, radiomic features that may be used for diagnostic, prognostic, and treatment response assessment of the tumor. The radiomic features may be shape-based features, intensity-based features, textural features, and filter-based features. The radiomic features are compared to remove sufficiently redundant features, thereby producing a reduced set of radiomic features, which is then compared to separate genomic data and/or outcome data to identify clinically and biologically significant radiomic features for diagnostic, prognostic, and treatment response assessment, other applications.

A dynamic image analysis apparatus includes a hardware processor that acquires an X-ray dynamic image including continuous frame images acquired by continuously capturing a living body having a heartbeat in time series; performs logarithmic conversion for a pixel value of the acquired X-ray dynamic image to create a logarithmically converted image; sets, as a reference frame image, one frame image based on a heartbeat phase in at least one of the X-ray dynamic image and the logarithmically converted image; calculates (i) a difference or ratio between the X-ray dynamic image as the reference frame image and the X-ray dynamic image as a comparative frame image which is another frame image or (ii) a difference or ratio between the logarithmically converted image as the reference frame image and the logarithmically converted image as the comparative frame image; and generates a blood flow analysis image.

Systems and methods of estimating movement of anatomical structures of a new patient include learning one or more deformation models from preoperative and intraoperative imaging data of a set of other patients and estimating movement of anatomical structures of the new patient based on the one or more deformation models and preoperative imaging data of the new patient. Estimating movement of the anatomical structures may include applying deformation models to map data derived from preoperative imaging data of a new patient to obtain deformed map data for each deformation model, and determining the deformation model that best fits the intraoperative imaging data of the new patient. Applying a deformation model to map data may include applying a transformation to the deformation model and interpolating to obtain a deformed map data. Registration is computed between locations of a medical device sampled during navigation of the medical device through the new patient and the original and deformed map data. The map data which fits the 3D locations the best is applied to the targets of the new patient.

A method comprises providing a pre-treatment image of a target subject to at least one deep learning model uniquely trained to predict immunotherapy treatment responses. The method further comprises generating, by a processing device, a predicted treatment response score to a treatment based on the single pre-treatment image and the at least one deep learning model. The method further comprises providing, based on the predicted treatment response score, a recommended treatment plan.

A quantification system (700) is described that includes: at least one input (710) configured to provide two input medical images and two locations of interest in said input medical images that correspond to a same anatomical region; and a mapping circuit (725) configured to compute a direct quantification of change of said input medical images from the at least one input (710).

Method of enrichment of a reference model to be enriched representing a dental arch. Acquisition, under first real conditions, of a current image of the arch displaying one region. Exploration of the reference model in such a manner as to determine a first view of the reference model, in a first direction of observation the reference image exhibiting a maximum match with the current image. Determination, by comparison of the images, of a first orphan point represented on the current image and not represented on the reference image when the current image is in a first register position in which it is superposed, in the space of the reference model, with the reference image. Addition, in the reference model, of a point on a first straight line parallel to the first direction of observation and passing through the first orphan point in the first register position.

A greenhouse information automatic monitoring method, adopting a multi-sensor system, using binocular vision multi-function cameras combining with a laser ranging sensor and an infrared temperature measuring sensor, realizing online patrol monitoring of greenhouse crop comprehensive information of image and infrared temperature characteristics of plant nutrition, water, pest and disease damage as well as plant crown width, plant height, fruit and growth characteristics. The multi-sensor system is mounted on a suspension slide platform and combines with a lifting mechanism and an electric control rotation pan-tilt, such that not only accurate positioning and stationary point detection in the detection travelling direction can be realized, but also multi-sensor information patrol detection at different detection distances, different top view fields and different detection angles is realized.

Provided herein are systems and methods for computer monitoring of remote photoplethysmography (rPPG) from camera images based on chromaticity in a converted color space, which reduces motion-induced artifacts in camera images for improved rPPG computer monitoring of physiological parameters. In particular, a rPPG system for monitoring at least one physiological parameter from image data is disclosed herein. A processor subsystem electronically receives a first image data set representative of a series of consecutive images of at least a portion of a living body. The processor subsystem converts the first image data set from a first color space to a second color space to generate a second image data set including first channel data comprising a luminance component and second channel data comprising a chromatic component. The processor subsystem processes the second channel data to monitor the at least one physiological parameter of the living body.