Disclosed is an X-ray image processing apparatus including a data obtaining unit generating first to N-th images indicating an internal structure of an object and an image processing unit receiving the first to N-th images from the data obtaining unit, detecting a movement of the object, and generating a final image from the first to N-th images based on the movement of the object. The data obtaining unit actively controls an X-ray pulse irradiated based on the movement of the object.

The disclosure herein relates to systems, methods, and devices for medical image analysis, diagnosis, risk stratification, decision making and/or disease tracking. In some embodiments, the systems, devices, and methods described herein are configured to analyze non-invasive medical images of a subject to automatically and/or dynamically identify one or more features, such as plaque and vessels, and/or derive one or more quantified plaque parameters, such as radiodensity, radiodensity composition, volume, radiodensity heterogeneity, geometry, location, and/or the like. In some embodiments, the systems, devices, and methods described herein are further configured to generate one or more assessments of plaque-based diseases from raw medical images using one or more of the identified features and/or quantified parameters.

A medical image processing apparatus according to an embodiment includes processing circuitry. The processing circuitry is configured to extract a degree of a disease related to the heart from a medical image; and display, when the degree of the disease related to the heart is high, a first index value related to a blood vessel and calculated from one of blood pressure and a blood flow and configured to display, when the degree of the disease related to the heart is low, wall shear stress serving as a second index value related to the blood vessel, as information related to the blood flow of the blood vessel and calculated on the basis of the medical image.

An object volume acquisition method of an ultrasonic image, for a probe of an ultrasonic system is disclosed. The volume acquisition method of the object in the ultrasonic image includes collecting, by the probe, a plurality of two-dimensional ultrasonic images; obtaining the plurality of two-dimensional ultrasonic images, an offset angle, a rotation axis and a frequency of the probe corresponding to the plurality of two-dimensional ultrasonic images; segmenting a first image including an ultrasonic image object from each two-dimensional ultrasonic image of the plurality of two-dimensional ultrasonic images based on a deep learning structure; determining a contour of the ultrasonic image object; reconstructing a three-dimensional model corresponding to the ultrasonic image object according to the contour of the ultrasonic image object corresponding to the each two-dimensional ultrasonic image; and calculating a volume of the ultrasonic image object according to the three-dimensional model corresponding to the ultrasonic image object.

A method of calculating leg length discrepancy of a patient including: receiving patient bone data associated with a lower body of the patient; identifying anatomical landmarks in the patient bone data; orienting a first proximal landmark and a second proximal landmark relative to each other and an origin in a coordinate system; aligning a first axis associated with a first femur and a second axis associated with a second femur with a longitudinal axis extending in a distal-proximal direction, wherein the first and second distal landmarks are adjusted according to the alignment of the first and second axes; calculating a distance between the first and second distal landmarks in the distal-proximal direction along the longitudinal axis; and displaying at least one of the distance or a portion of the patient bone data on a display screen.

Embodiments discussed herein facilitate generation of a prognosis for recurrence or non-recurrence of atrial fibrillation (AF) after pulmonary vein isolation (PVI). A first set of embodiments discussed herein relates to training of a machine learning classifier to determine a prognosis for AF after PVI based on radiographic images, alone or in combination with clinical features. A second set of embodiments discussed herein relates to determination of a prognosis for a patient for AF after PVI based on radiographic images, alone or in combination with clinical features.

An X-ray image processing method, including obtaining a first X-ray image of an object including a plurality of materials including a first material and a second material; obtaining a first partial image generated by imaging the first material and a second partial image generated by imaging the first material overlapping the second material from the first X-ray image; obtaining first information related to a stereoscopic structure of the first material, based on the first partial image included in the first X-ray image; and obtaining second information about the second material based on the first information and the second partial image.

A computer-implemented method and system of automatically detecting and removing extraneous material from a digital dental impression includes detecting one or more dental features in a digital dental impression, filtering the one or more dental features, digitally joining the one or more dental features, and determining one or more regions of interest from the joined one or more digital dental features.

A method for operating a medical imaging device includes obtaining lesion information on at least one lesion detected from a medical image, determining a shape and a position of at least one contour corresponding to the at least one lesion based on the obtained lesion information, determining a position of at least one text region that includes a text indicating the lesion information on the at least one lesion in the medical image, and displaying the at least one contour and the text included in the at least one text region on the medical image, based on the determined shape and position of the at least one contour and the determined position of the at least one text region.

Systems and methods are provided to plan a spinal correction surgery. The method includes measuring parameters of a spine in a two-dimensional (2D) spinal image including a thoracic Cobb angle and a thoracic kyphosis (TK) and transforming the 2D image to a three-dimensional (3D), spinal image representation. The transforming includes performing segmentation of spine elements in the 2D image, and applying a formula based on the thoracic Cobb angle and the TK to the spine elements. The method includes identifying a TK goal having a post-operative TK value to selected spine elements, transforming a gap of the spine elements representative of a difference between the pre-operative TK in 3D spinal image representation and the TK goal to create a 3D post-operative spinal image representation, and determining a first rod design based on the 3D post-operative spinal image representation to achieve the post-operative TK value in the spine elements.