One or more example embodiments of the present invention relates to a computer-implemented method for providing an outline of a lesion in digital breast tomosynthesis includes receiving input data, wherein the input data comprises a reconstructed tomosynthesis volume dataset based on projection recordings, a virtual target marker within a lesion being in the tomosynthesis volume dataset; applying a trained function to at least a part of the tomosynthesis volume dataset to establish an outline enclosing the lesion, the part of the tomosynthesis volume dataset corresponding to a region surrounding the virtual target marker in the tomosynthesis volume dataset; and providing output data, wherein the output data is an outline of a two-dimensional area or a three-dimensional volume surrounding the target marker.

A method for acquiring at least one two-dimensional image of a part of arches of a patient includes steps carried out by the patient or other person who is not a dental health professional, for example, including placing a dental separator in the mouth of the patient in order to separate the lips of the patient and improve the visibility of the teeth during the acquisition of said at least one two-dimensional image, and acquiring, in a mouth closed position and with a personal image acquisition apparatus, said at least one two-dimensional image.

An intraoral imaging apparatus, a medical apparatus, and a program capable of providing auxiliary data for determination regarding diseases having differences in intraoral findings are provided. The intraoral imaging apparatus includes: an imaging device that acquires an intraoral image; a light source that emits light to a subject of the imaging device; a storage apparatus that stores an algorithm for performing determination of a specific disease; and an arithmetic apparatus, in which the arithmetic apparatus executes: a determination process of determining a possibility of the predetermined disease based on the image and the algorithm; and an output process of outputting a result of the determination process.

A method for determining an abnormality in a medical device from a medical image is provided. The method for determining an abnormality in a medical device comprises receiving a medical image, and detecting information on at least a part of a target medical device included in the received medical image.

The invention relates to the field of computer engineering for processing images that provides increased accuracy of finding and classifying a similar object . The technical result is achieved by: downloading files of a radiographic image which comprise metadata including information about the object or subject of the image and information about the image itself; encrypting the downloaded files if the above-mentioned files comprise personal data about a person; decrypting the above-mentioned, encrypted, downloaded files; and processing the radiographic image, wherein, as a result of the processing, the following occurs: finding and capturing a relevant region of the radiographic image; removing noise from the captured, relevant region of the radiographic image, wherein a region with a found object is meant by a relevant region of the radiographic image; compressing or unzipping a previously processed radiographic image; and finding a similar object in two previously processed images, and processing said object.

An image processing system includes a processor, the processor performing processing, based on association information of an association between a biological image captured under a first imaging condition and a biological image captured under a second imaging condition, of outputting a prediction image corresponding to an image in which an object captured in an input image is to be captured under the second imaging condition. The association information is indicative of a trained model obtained through machine learning of a relationship between a first training image captured under the first imaging condition and a second training image captured under the second imaging condition. The processor is capable of outputting a plurality of different kinds of prediction images based on a plurality of trained models and the input image, and performs processing, based on a given condition, of selecting the prediction image to be output among a plurality of prediction images.

The present invention relates to a method of processing CT data for suppressing image cone beam artefacts (CBA) in CT images, which are reconstructed from said CT data. For the reconstruction the Frequency Split method is used. However, a straightforward use of this method can lead to an un-desired increase of the residual low-frequency noise left in the basis image after applying image domain de-noising methods. This residual noise then propagates rather linearly to the spectral results. In order to avoid this increase of the noise, the method presented here uses the FS method selectively and yet effectively. Thus, in a first aspect of the invention there is provided a method of processing computer tomography (CT) data for suppressing image cone beam artefacts (CBA) in CT images to be reconstructed from said CT data. The method comprises the steps of obtaining CT data generated during a CT scan of a patient (step S1); decomposing the obtained CT data in the projection domain resulting in a plurality of decomposed sinograms (step S2); and non-uniformly spreading between said decomposed sinograms noise and/or inconsistencies that would lead to image cone beam artefacts (step S3).

The application relates to devices and methods for analysing a colonoscopy video or a portion thereof, and for assessing the severity of ulcerative colitis in a subject by analysing a colonoscopy video obtained from the subject. Analysing a colonoscopy video comprises using a first deep neural network classifier to classify image data from the subject colonoscopy video or portion thereof into at least a first severity class (more severe endoscopic lesions) and a second severity class (less severe endoscopic lesions), wherein the first deep neural network has been trained at least in part in a weakly supervised manner using training image data from a plurality of training colonoscopy videos, the training image data comprising multiple sets of consecutive frames from the plurality of training colonoscopy videos, wherein frames in a set have the same severity class label. Devices and methods for providing a tool for analysing colonoscopy videos are also described.

In one implementation, a computer-implemented system is provided for real- time video processing. The system includes at least one memory configured to store instructions and at least one processor configured to execute the instructions to perform operations. The at least one processor is configured to receive real-time video generated by a medical image system, the real-time video including a plurality of image frames, and obtain context information indicating an interaction of a user with the medical image system. The at least processor is also configured to perform an object detection to detect at least one object in the plurality of image frames and perform a classification to generate classification information for at least one object in the plurality of image frames. Further, the at least one processor is configured to perform a video manipulation to modify the received real-time video based on at least one of the object detection and the classification. Moreover, the processor is configured to invoke at least one of the object detection, the classification, and the video manipulation based on the context information.

In a general aspect, enhancement of artificial intelligence algorithms using 3D data is described. In some aspects, input data of an object is stored in a storage engine of a system. The input data includes first-order primitives and second-order primitives. A plurality of features of the object is determined by operation of an analytics engine of the system, based on the first-order primitives and the second-order primitives. A tensor field is generated by operation of the analytics engine of the system. The tensor field includes an attribute set, which includes one or more attributes selected from the first-order primitives, the second-order primitives, or the plurality of features. The tensor field is processed by operation of the analytics engine of the system according to a series of artificial intelligence algorithms to generate output data representing the object.