Various methods and systems are provided for artifact reduction with resolution preservation. In one example, a method includes obtaining projection data of an imaging subject, identifying a metal-containing region in the projection data, interpolating the metal-containing region to generate interpolated projection data, extracting high frequency content information from the projection data in the metal-containing region, adding the extracted high frequency content information to the interpolated projection data to generate adjusted projection data, and reconstructing one or more diagnostic images from the adjusted projection data.

A first electronic device according to various embodiments may include: a display; a communication module comprising communication circuitry; a camera module including at least one camera; and a processor. The processor may be configured to: identify a request for measuring a skin condition of a user in a state in which the first electronic device is cradled on a second electronic device; acquire, based on information of the camera module and information regarding at least one light-emitting element included in the second electronic device, control information for controlling output of the at least one light-emitting element; control output of light from the at least one light-emitting element of the second electronic device based on the control information; acquire at least one image including at least a part of a body of the user through the camera module while light is output through the at least one light-emitting element controlled based on the control information; and provide information regarding the skin condition of the user using the at least one image.

The invention relates to a computer-implemented method for automatically detecting anatomical structures (3) in a medical image (1) of a subject, the method comprising applying an object detector function (4) to the medical image, wherein the object detector function performs the steps of: (A) applying a first neural network (40) to the medical image, wherein the first neural network is trained to detect a first plurality of classes of larger-sized anatomical structures (3a), thereby generating as output the coordinates of at least one first bounding box (51) and the confidence score of it containing a larger-sized anatomical structure; (B) cropping (42) the medical image to the first bounding box, thereby generating a cropped image (11) containing the image content within the first bounding box (51); and (C) applying a second neural network (44) to the cropped medical image, wherein the second neural network is trained to detect at least one second class of smaller-sized anatomical structures (3b), thereby generating as output the coordinates of at least one second bounding box (54) and the confidence score of it containing a smaller-sized anatomical structure.

A method, device, computer system and computer readable storage medium for 3D reconstruction are provided. The method comprises: performing a 3D reconstruction of an original 2D image of a target object to generate an original 3D object corresponding to the original 2D image; selecting a complementary view of the target object from candidate views based on a reconstruction quality of the original 3D object at the candidate views; obtaining a complementary 2D image of the target object based on the complementary view; performing a 3D reconstruction of the complementary 2D image to generate a complementary 3D object corresponding to the complementary 2D image; and fusing the original 3D object and the complementary 3D object to obtain a 3D reconstruction result of the target object.

The invention relates to a computing system and a computer-implemented method for classifying images of retina of eyes of subjects. A captured image of a retina is processed to obtain a plurality of different segmented images each having different selected portions of the captured image using different selection rules. The multiple segmented images are provided to respective dedicated machine learning models to output an image classification based on the respective segmented images provided as input. An ensemble classification is determined based on the multiple classifications obtained by means of the multiple trained machine learning models.

In some embodiments, a non-transitory processor-readable medium stores code representing instructions to be executed by a processor. The code includes code to cause the processor to receive a plurality of sets of images associated with a sample treated with fluorescence in situ hybridization (FISH) probes. Each image from that set of images is associated with a different focal length using a fluorescence microscope. Each FISH probe can selectively bind to a unique location on chromosomal DNA in the sample. The code further causes the processor to identify cell nuclei in the images. The code further causes the processor to apply a convolutional neural network (CNN) to each set of images. The CNN is configured to identify a probe indication from a plurality of probe indications for that set of images. The code further causes the processor to identify the sample as containing circulating tumor cells.

A dental medical record device and a dental medical record method, in which: an image, such as a panoramic photo, a scan image, and a camera image of a patient's oral cavity, is received via artificial intelligence, and charting is performed using the artificial intelligence; and medical records for a treatment area can be read in association with a chart by clicking the treatment area in the image.

A surgical operation support apparatus 1 for performing effective display with respect to a target site image according to the situation has a detection unit 2 that detects a target site image from an image capturing an inside of a living body, an estimation unit 3 that estimates whether the target site image is being observed, and a display information generation unit 4 that generates a first display for facilitating observation of the target site image, if it is estimated that the target site image is being observed.

The present invention discloses a method for selective crop management in real time. The method comprises steps of: (a) producing a biosensor plant, said biosensor plant comprises a visual biomarker, said biomarker is encoded by at least one modified genetic locus comprising (i) preselected reporter gene allele having a phenotype detectable by a sensor, and (ii) a regulatory region of a preselected gene allele responsive to at least one parameter or condition of said plant or its environment, said regulatory region is operably linked to said reporter gene, such that the expression of said reporter gene phenotype is correlated with the status of said at least one parameter or condition of said biosensor plant or its environment; (b) acquiring image data of a target area comprising a plurality of said biosensor plants via said sensor and processing said data to generate a signal indicative of the phenotypic expression of said reporter gene allele of said biosensor plant; and (c) communicating said signal to an execution unit communicably linked to the sensor, said execution unit is capable of exerting in real time a selective monitoring and/or treatment of said target area or a portion thereof comprising said biosensor plants, said treatment is being responsive to said status of said parameter or condition of the biosensor plant or its environment. The present invention further discloses systems and plants related to the aforementioned method.

A program and the like that make a catheter system relatively easy to use. The program including a non-transitory computer-readable medium (CRM) storing computer program code executed by a computer processor that executes a process comprising: acquiring a tomographic image generated using a diagnostic imaging catheter inserted into a lumen organ; and inputting the acquired tomographic image to a first model configured to output types of a plurality of objects included in the tomographic image and ranges of the respective objects in association with each other when the tomographic image is input, and outputting the types and ranges of the objects output from the first model.