The present disclosure directs to a method for image processing. The method may include obtaining scanning data of a spine of a subject, determining one or more centrum parameters of each of a plurality of centrums of the spine based on the scanning data, and identifying at least one intervertebral disc based on the one or more centrum parameters.

Each of the at least one intervertebral disc may be between a pair of neighboring centrums of the plurality of centrums. The method may include determining an intervertebral disc reconstruction protocol of each of the at least one intervertebral disc, determining a target intervertebral disc of the at least one intervertebral disc, and reconstructing one or more images of the target intervertebral disc based on an intervertebral disc reconstruction protocol of the target intervertebral disc. The intervertebral disc reconstruction protocols may relate to MPR.

A method of determining axial alignment of a lower limb includes determining a three-dimensional volume based on a scan of a lower limb of a patient, identifying a level of hip version by referring to a first axis of symmetry of a femoral neck and a second axis of symmetry of a distal portion of a femur, wherein hip version is defined by an angle between the first axis of symmetry and the second the second axis of symmetry, identifying a level of tibial torsion by referring to a third axis of symmetry of a proximal portion of a tibia and a fourth axis of symmetry of a distal portion of the tibia, wherein tibial torsion is defined by an angle between the third axis of symmetry and the fourth axis of symmetry, and providing the level of hip version and the level of tibial torsion to a user.

A device may receive an X-ray image captured by a C-arm CBCT device at a particular position defined by a six-degree of freedom pose relative to an anatomy, and may process the X-ray image, with a machine learning model, to determine a predicted quality of next possible X-ray images provided by the C-arm CBCT device. The device may utilize the machine learning model, to identify a particular X-ray image, of the next possible X-ray images, with a greatest predicted quality and to update the six-degree of freedom pose based on the particular X-ray image. The device may provide, to the C-arm CBCT device, data that identifies the updated six-degree of freedom pose to cause the C-arm CBCT device to adjust to a new position based on the updated six-degree of freedom pose.

Disclosed is surface guided cropping in volume rendering of 3D volumetric data from intervening anatomical structures in the patient's body. A digital 3D representation expressing the topography of a first anatomical structure is used to define a clipping surface or a bounding volume which then is used in the volume rendering to exclude data from an intervening structure when generating a 2D projection of the first anatomical structure.

An X-ray diagnosis apparatus controls to display information based on an error in an index value evaluating a state of a bone of an object based on at least one of a captured image of the object and an imaging condition which correspond to X-rays with two different types of energies.

Provided is a 3D bone density and bone age calculation apparatus using an artificial intelligence-based rotation manner. The 3D bone density and bone age calculation apparatus includes a main body, and the main body includes a rotary drum including a drum shaft gear, an X-ray generator, an intensifying screen, and an image data capturer, a drum driver including a motor shaft gear connected to the drum shaft gear so as to rotate the rotary drum, a motor, support rollers and one of an origin sensor and an encoder, an outer case and an inner case, a front case and a rear case, a capturing holder, and a controller configured to select an image-captured position of the rotary drum, and configured to input a current age, sex and nutritional status of a patient, etc. The controller includes a display configured to display captured images and a diagram indicating bone age.

Noise is reduced for a medical image for which noise cannot be quantified by a general-purpose image quality evaluation index. An image processor has a preprocessor that generates input images including an original image and one or more images with reduced noise compared with the original image; and a noise reduction processor outputs an image, which is obtained by reducing noise from the original image based on the input images, by applying a learned network. The learned network used in the noise reduction processor is constructed by performing deep learning using a plurality of learning sets in which one or more of a medical image including noise, a noise-reduced image obtained by performing noise reduction processing on the medical image, and an intermediate image obtained during the noise reduction processing are input images and a correct image is obtained based on the input images an output image.

A failed-image decision support apparatus includes a hardware processor and an outputter. The hardware processor performs, among multiple types of failed-image determination processes, at least one failed-image determination process on a medical image, thereby generating a determination result and determination basis information indicating a basis for the determination result. The outputter outputs the determination result and the determination basis information. Another failed-image decision support apparatus includes a hardware processor. The hardware processor performs, among multiple types of failed-image determination processes, at least one failed-image determination process on a medical image, thereby generating a determination result and determination basis information indicating a basis for the determination result, and controls output of the determination result and the determination basis information.

A medical scan assisted review system is operable to receive, via a network, a medical scan for review. Abnormality data is generated by identifying a plurality of abnormalities in the medical scan by utilizing a computer vision model that is trained on a plurality of training medical scans. The abnormality data includes location data and classification data for each of the plurality of abnormalities. Text describing each of the plurality of abnormalities is generated based on the abnormality data. The abnormality data and the text is transmitted to a client device. A display device associated with the client device displays the abnormality data in conjunction with the medical scan via an interactive interface, and the display device further displays the text via the interactive interface.

A surgical planning system for use in surgical procedures to repair an anatomy of interest includes a preplanning system to generate a virtual surgical plan and a mixed reality system that includes a visualization device wearable by a user to view the virtual surgical plan projected in a real environment. The virtual surgical plan includes a 3D virtual model of the anatomy of interest. When wearing the visualization device, the user can align the 3D virtual model with the real anatomy of interest, thereby achieving a registration between details of the virtual surgical plan and the real anatomy of interest. The registration enables a surgeon to implement the virtual surgical plan on the real anatomy of interest without the use of tracking markers.