A non-transitory computer-readable medium (CRM) storing computer program code executed by a computer processor that executes a process of acquiring a medical image generated based on a signal detected by a catheter inserted to a lumen organ, estimating a position of an object at least included in the acquired medical image by inputting the medical image to a first learning model for estimating a position of an object included in the medical image, extracting from the medical image an image portion by using the estimated position of the object as a reference, and recognizing the object included in the extracted image portion by inputting the image portion to a second learning model for recognizing an object included in the image portion.

Determination of presence or absence of a defect having irregular position, size, shape, and/or the like in an image are made automatically. An inspection device includes: an inspection image obtaining section that obtains an inspection image used to determine presence or absence of an internal defect in an inspection target; and a defect presence/absence determining section that determines presence or absence of a defect with use of a restored image generated by inputting the inspection image into a generative model constructed by machine learning that uses, as training data, an image of an inspection target in which a defect is absent, the generative model being constructed so as to generate a new image having a similar feature to that of an image input into the generative model.

An information processing device including: a control unit configured to: acquire at least one frame image generated by using line data indicating intensity of a reflection wave with respect to an ultrasound radially transmitted from an ultrasound transducer moving inside a biological tissue, and extract a specific region from the at least one frame image based on a ratio between an average of luminance in a region of interest included in the at least one frame image and a dispersion of luminance in the region of interest.

A system for performing computer-assisted image analysis of welds and related methods is disclosed. Digital images are captured at a worksite and sent to a remote image analysis system of a weld analytics system that analyzes images to determine whether the images conform to weld specifications. The remote image analysis system may be trained by artificial intelligence or machine learning.

Systems and methods for analyzing pathologies utilizing quantitative imaging are presented herein. Advantageously, the systems and methods of the present disclosure utilize a hierarchical analytics framework that identifies and quantify biological properties/analytes from imaging data and then identifies and characterizes one or more pathologies based on the quantified biological properties/analytes. This hierarchical approach of using imaging to examine underlying biology as an intermediary to assessing pathology provides many analytic and processing advantages over systems and methods that are configured to directly determine and characterize pathology from underlying imaging data.

A local extension method for segmentation of anatomical treelike structures includes receiving an initial segmentation of 3D image data including an initial treelike structure. A target point in the 3D image data is defined, and a region of interest based on the target point is extracted to create a sub-image. Highly tubular voxels are detected in the sub-image, and a spillage-constrained region growing is performed using the highly tubular voxels as seed points. Connected components are extracted from the results of the region growing. The extracted components are pruned to discard components not likely to be connected to the initial treelike structure, keeping only candidate components likely to be a valid sub-tree of the initial treelike structure. The candidate components are connected to the initial treelike structure, thereby extending the initial segmentation in the region of interest.

Provided are an ultrasound imaging apparatus and an operation method for registering an ultrasound image and an image from another modality. The ultrasound imaging apparatus may register the ultrasound image and the image from the other modality based on a three-dimensional positional relationship between at least one external electromagnetic sensor attached to a patient's body and an ultrasound probe and on a position of a feature point extracted from the image from the other modality.

A plurality of ultrasound frames of a fetus are acquired using an ultrasound scanner, which may be oriented arbitrarily with respect to the fetus during the acquisition. The ultrasound frames are processed against an artificial intelligence model to predict a different cut line on each of the ultrasound frames. Each cut line is predicted to be exterior to an image of the fetus appearing on the ultrasound frame. The different cut lines on the plurality of ultrasound frames are then used to identify ultrasound data in the image frames to generate a 3D representation of the fetus.

A system and method of computer-aided detection (CAD or CADe) of medical images that utilizes persistence between images of a sequence to identify regions of interest detected with low interference from artifacts to reduce false positives and improve probability of detection of true lesions, thereby providing improved performance over static CADe methods for automatic ROI lesion detection.

A processor acquires an ultrasound image group including a plurality of ultrasound images of a subject captured by an ultrasound probe, which is moved along a body surface of the subject, at a plurality of imaging positions of the subject. The processor searches for a similar ultrasound image group that is similar to features of the acquired ultrasound image group with reference to a plurality of diagnosed ultrasound image groups and a diagnostic log which is for each of the plurality of diagnosed ultrasound image groups and which includes positional information indicating a position of a disease. The processor specifies an imaging position on a body surface where a disease predicted in the subject is capable of being imaged, using the positional information included in the diagnostic log for the similar ultrasound image group, and notifies of the specified imaging position.