The present invention relates to an image processing system (10), comprising: a processor unit (20) arranged to receive imaging data associated with an imaging system (40) and optical shape sensing data associated with an optical shape sensing system (50) registered with the imaging system (40) such that the optical shape sensing data can be positioned in the imaging system; wherein the processor unit (20) is configured to define in the imaging data a region of interest based on the imaging data and/or the optical shape sensing data and further configured to use the optical shape sensing data as markers within the region of interest such that the processor unit applies image enhancement of imaging data on the region of interest based on received optical shape sensing data.

Embodiments of the present disclosure are related to providing a method and device processing a first set of volumetric image data comprising cross-sectional images of a myocardium and displaying a second set of volumetric image data of the myocardium. A curved plane to rectangular plane transformation of cross-sectional images of myocardium of human heart is proposed. After the transformation, a combined and reconstructed set of myocardium images are superimposed with a modified Bull's Eye View (BEV) map and corresponding parameters indicating extent of fibrosis to obtain a second set of volumetric image data of myocardium. In addition to quantifying and displaying the extent of fibrosis, the proposed solution preserves neighborhood and adjacency criteria of abnormal tissues of myocardium walls of human heart.

A method for detecting structures is provided. The method can include receiving inspection image data characterizing a region of interest of an object being inspected. The regions of interest can include one or more structures of the object. The method can also include determining, using a computer vision algorithm, a structure within the region of interest with respect to photometric properties of pixel data in the inspection image data. The structure can be determined using a predictive model trained to determine image filter parameter values for image filters of the computer vision algorithm based on applying optimization techniques using training image data and annotation data. An indication of the structure can be provided, for example for display or storage in memory. Systems and computer-readable mediums implementing the method are also provided.

A diagnostic assistance apparatus according to an aspect of the present disclosure determines whether a body part of a target examinee captured in a target medical image is normal, by using a trained first classification model generated by unsupervised learning using a plurality of first learning medical images of normal cases and a trained second classification model generated by supervised learning using a plurality of learning data sets including normal cases and abnormal cases.

Various methods and systems are provided for real-time image segmentation of medical image data. In one example, the real-time image segmentation of the medical image data may include updating an initial segmentation of the medical image data in real-time. The update may be based on a user input to a regularization brush applied to the medical image data, the user input to the regularization brush allowing modification of a volume of the initial segmentation.

A computer implemented method and apparatus for determining a location at which a given feature is represented in medical imaging data is disclosed. A first descriptor for a first location in first medical imaging data is obtained. The first location is the location within the first medical imaging data at which the given feature is represented. A second descriptor for each of a plurality of candidate second locations in second medical imaging data is obtained. A similarity metric indicating a degree of similarity with the first descriptor is calculated for each of the plurality of candidate second locations. A candidate second location is selected from among the plurality of candidate second locations based on the calculated similarity metrics. The location at which the given feature is represented in the second medical imaging data is determined based on the selected candidate second location.

The present disclosure provides methods and systems directed to performing real-time and/or AI-assisted radiology. A method for processing an image of a location of a body of a subject may comprise (a) obtaining the image of the location of a body of the subject; (b) using a trained algorithm to classify the image or a derivative thereof to a category among a plurality of categories, wherein the classifying comprises applying an image processing algorithm; (c) directing the image to a first radiologist for radiological assessment if the image is classified to a first category among the plurality of categories, or (ii) directing the image to a second radiologist for radiological assessment, if the image is classified to a second category among the plurality of categories; and (d) receiving a recommendation from the first or second radiologist to examine the subject based at least in part on the radiological assessment.

A radiation imaging system comprises: an obtainment unit configured to obtain an image captured by radiation imaging; an image processing unit configured to generate a radiation image by applying image processing to the captured image; a display control unit configured to display, on a display unit, the radiation image with the image processing applied thereto; and a control unit configured to determine, based on an operation input, whether confirmation of the radiation image displayed on the display unit is complete.

A CPU acquires a distance image or a visible light image obtained by imaging a marker for measuring an SID as an object to be imaged using a TOF camera or a visible light camera. In addition, the CPU derives a marker distance between the TOF camera or the visible light camera and the marker from an image of a marker region corresponding to the marker in the acquired distance image or visible light image. Further, the CPU derives the SID on the basis of the derived marker distance and information indicating a positional relationship between an acquisition unit and the marker.

A CPU acquires a distance image or a visible light image captured by a TOF camera or a visible light camera that has, as an imageable region, a region including an irradiation region which is a space in which a breast of a subject imaged by a mammography apparatus is irradiated with radiation emitted from a radiation source and detects whether or not a foreign object other than an object to be imaged is present in the irradiation region on the basis of the distance image or the visible light image.