A method of medical image registration is to be implemented by a computer device, and includes steps of: obtaining an ultrasound target image corresponding to an area of interest in an ultrasound image; for each of multiple computed tomography (CT) images, obtaining a CT candidate image that corresponds to an area of interest in the CT image; for each of the CT candidate images, calculating a similarity between the CT candidate image and the ultrasound target image; making one of the CT candidate images that corresponds to the greatest similarity among the CT candidate images serve as a CT target image; and performing image registration on the ultrasound target image and the CT target image.

Systems and methods for providing assistance to a surgeon during an implant surgery are disclosed. A method includes defining areas of interest in diagnostic data of a patient and defining a screw bone type based on the surgeon's input. Post defining the areas of interest, salient points are determined for the areas of interest. Successively, an XZ angle, an XY angle, and a position entry point for a screw are determined based on the salient points of the areas of interest. Successively, a maximum screw diameter and a length of the screw are determined based on the salient points. Thereafter, the screw is identified and suggested to the surgeon for usage during the implant surgery.

A method and system for automatically labeling a spine image is disclosed. The method includes receiving an input spine image and analyzing image features of the input spine image by a deep neural network. The method further includes generating a mask image corresponding to the input spine image by the deep neural network based on image characteristics of a training image dataset. A region of interest in the mask image comprises vertebral candidates of the spine. The training image dataset comprises a plurality of spine images and a plurality of corresponding mask images. The method further includes associating labels with a plurality of image components of the mask image and labeling the input spine image based on labels associated with the mask image.

The present invention relates to an apparatus (10) for feature suppression in dark field or phase contrast X-ray imaging. The apparatus comprises an input unit (20), a processing unit (30) and an output unit (40). The input unit is configured to provide the processing unit with an X-ray attenuation image of a region of interest of an object. The input unit is also configured to provide the processing unit with a dark field or phase contrast X-ray image of the region of interest of the object. The processing unit is further configured to identify a first feature in the X-ray attenuation image; to identify a second anatomical feature in the X-ray attenuation image; and to identify the second anatomical feature in the dark field or phase contrast X-ray image. The first feature is an obscuring anatomical feature depicted in the X-ray attenuation image with higher contrast than in the dark field or phase contrast X-ray image. The processing unit is also further configured to register the dark field or phase contrast X-ray image to the X-ray attenuation image based on the identified second anatomical feature. The processing unit is configured to determine a location of the first feature in the X-ray attenuation image; and to locate the first feature in the dark field or phase contrast X-ray image comprising utilization of information relating to the first feature identified in the X-ray attenuation image by transferring the determined location to the dark field or phase contrast X-ray image. The processing unit is still further configured to suppress the first feature in the dark field or phase contrast X-ray image to generate a feature suppressed dark field or phase contrast X-ray image. And the output unit is configured to output data representative of the feature suppressed dark field or phase contrast X-ray image.

Disclosed are systems and methods for rapid generation of simulations of a patient's spinal morphology that enable pre-operative viewing of a patient's condition and to assist surgeons in determining the best corrective procedure and with any of the selection, augmentation or manufacture of spinal devices based on the patient specific simulated condition. The simulation is generated by morphing a generic spine model with a three-dimensional curve representation of the patient's particular spinal morphology derived from existing images of the patient's condition. Other anatomical structures in the patient's skeletal system are likewise simulated by morphing a generic normal skeletal model, as applicable, particularly those skeletal entities that are connected directly or indirectly to the spinal column.

An image processing apparatus according to the present embodiment comprises processing circuitry configured to acquire a medical image regarding a spine including a plurality of vertebrae, receive, from a user, an input operation of editing a spine number allocated to each of the vertebrae in the medical image, and correct the spine number other than the spine number edited based on the received input operation.

A medical image processing apparatus includes a memory; and at least one processor configured to execute detecting one or more vertebral bodies and one or more intervertebral disks in a medical image; labeling each part satisfying a predetermined condition among the one or more vertebral bodies and the one or more intervertebral disks detected by the detecting; interpolating a vertebral body or an intervertebral disk in a case where the one or more vertebral bodies and the one or more intervertebral disks detected by the detecting do not include the vertebral body or the intervertebral disk that satisfies the predetermined condition; and executing the labeling also for the vertebral body or the intervertebral disk interpolated by the interpolating.

A method of planning the correction of spinal deformations of a subject, by performing segmentation on a three dimensional image of the subject's spine in its erect neutral position, such that the positions and orientations of the vertebrae in a region of interest are characterized. Parameters relating to the alignment and position of the vertebrae are derived from the segmentation, followed by determining whether the parameters fall within an acceptable range desired for the spine of the subject. If not within the acceptable range, an alignment optimization is performed on the vertebrae to bring the parameters within the acceptable range, to reduce the spinal deformations of the subject's spine. The alignment optimization is performed by taking into consideration limitations arising from the dynamic range of motion of the vertebrae as determined by analyzing images of the subject's spine, while the subject is in positions of maximum bending.

Apparatus and methods are described including acquiring 3D image data of a targeted skeletal portion within a body of a subject, and a 2D radiographic image of the targeted skeletal portion. A machine-learning engine is used to generate machine-learning data based on (i) the 3D image data of the targeted skeletal portion, (ii) a database of 2D projection images generated from the 3D image data, and (iii) respective values of one or more viewing parameters corresponding to each 2D projection image. A computer processor receives the machine-learning data, receives the 2D radiographic image of the targeted skeletal portion, and registers the 2D radiographic image to the 3D image data by using the machine-learning data to find a 2D projection from the 3D image data that matches the 2D radiographic image of the targeted skeletal portion. Other applications are also described.

Medical imaging systems and methods for representing a 3D volume containing at least one foreign object introduced into a tissue. Imaging methods may include provision of a 3D volume containing voxels of at least one foreign object and voxels of tissue surrounding the at least one foreign object, identification of the voxels of the at least one foreign object by application of a processing rule, segmentation of the voxels of the at least one foreign object from the voxels of the tissue surrounding the at least one foreign object while maintaining the 3D volume, generation of a synthetic volume from a residual volume and the volume of the at least one foreign object, and representation of the synthetic volume on a display device using a windowing system.