A method comprising segmenting at least one vertebral body from at least one image of a first three-dimensional image data set. The method comprises receiving at least one image of a second three-dimensional image data set. The method comprises registering the segmented at least one vertebral body from the at least one image of the first three-dimensional image data set with the at least one image of the second three-dimensional image data set. The method comprises determining a position of the at least one surgical implant based on the at least one image of the second three-dimensional image data set and a three-dimensional geometric model of the at least one surgical implant. The method comprises overlaying a virtual representation of the at least one surgical implant on the registered and segmented at least one vertebral body from the at least one image of the first three-dimensional image data set.

There is provided a method for predicting risk of osteoporotic fracture, comprising: receiving imaging data of a computed tomography (CT) scan of a body of a patient containing at least a bone portion, the CT scan being performed with settings selected for imaging of non-osteoporosis related pathology; processing the imaging data to identify the bone portion; automatically extracting features based on the imaging data denoting the identified bone portion; computing an osteoporotic fracture predictive factor indicative of the risk of developing at least one osteoporotic fracture in the patient, or the risk of the patient having at least one severe osteoporotic fracture, based on the extracted features, the predictive factor calculated by applying a trained osteoporotic fracture classifier to the extracted features, the osteoporotic fracture classifier trained from data from a plurality of CT scans performed with settings selected for imaging non-osteoporosis related pathology; and providing the predictive factor.

An embodiment in accordance with the present invention provides a technique for localizing structures of interest in projection images (e.g., x-ray projection radiographs or fluoroscopy) based on structures defined in a preoperative 3D image (e.g., MR or CT). Applications include, but are not limited to, spinal interventions. The present invention achieves 3D-2D image registration (and particularly allowing use with a preoperative MR image) by segmenting the structures of interest in the preoperative 3D image and generating a simulated projection of the segmented structures to be aligned with the 2D projection image. Other applications include various clinical scenarios involving 3D-2D image registration, such as image-guided cranial neurosurgery, orthopedic surgery, biopsy, and radiation therapy.

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 for incorporating tomographically obtained image data from a patient into a system for surgical planning and/or intraoperative navigation involves tomographic image data or image data obtained by X-ray recordings from at least one defined body area of the patient by at least one first recording appliance, wherein a first reference body having at least one surface is arranged on the patient and is recorded by the first recording appliance at the same time. The recorded image data representing the first reference body are compared with known geometric data from the first reference body in order to obtain distortion information. The recorded image data are equalized by a computation unit based on the distortion information to obtain equalized image data which have further image data from the same body area superimposed to obtain superimposed image data that is presented on a display.

A method and apparatus for automated vertebra localization and identification in a 3D computed tomography (CT) volumes is disclosed. Initial vertebra locations in a 3D CT volume of a patient are predicted for a plurality of vertebrae corresponding to a plurality of vertebra labels using a trained deep image-to-image network (DI2IN). The initial vertebra locations for the plurality of vertebrae predicted using the DI2IN are refined using a trained recurrent neural network, resulting in an updated set of vertebra locations for the plurality of vertebrae corresponding to the plurality of vertebrae labels. Final vertebra locations in the 3D CT volume for the plurality of vertebrae corresponding to the plurality of vertebra labels are determined by refining the updated set of vertebra locations using a trained shape-basis deep neural network.

The present invention relates to the technical field of medical image processing and, in particular, to a liver boundary identification method and a system. The method includes: obtaining liver tissue information of a liver tissue to be identified; identifying a liver tissue boundary in the liver tissue information according to a feature of the liver tissue corresponding to the liver tissue information and a feature of the liver tissue boundary corresponding to the liver tissue information using an image processing technology or a signal processing technology; and outputting position information of the identified liver tissue boundary. By using the disclosed method, the liver tissue boundary can be identified automatically, the efficiency of identifying the liver boundary can be improved, and automatic positioning of the liver boundary can thus be achieved.

Image processing and analysis technique includes using a computer apparatus to assess a patient's magnetic resonance images or derived multiparametric maps for pathology and then automatically generate a prescription based at least in part on that assessment. The parametric maps are derived from an MRI sequence from which multiparametric maps are derivable.

The present disclosure directs to a system and method for image processing. The method for image processing comprises acquiring a plurality of original computed tomography (CT) images of a spine of a subject; generating CT value images of the spine of the subject by processing the plurality of original CT images. The method further includes identifying an optimal sagittal image in which a centerline of the spine is located based on the CT value images. The method further includes identifying the centerline of the spine within the optimal sagittal image. The method further includes identifying a center point and a direction of at least one intervertebral disc along the centerline of the spine. The method still further includes reconstructing an image of the at least one intervertebral disc based on the center point and the direction of the at least one intervertebral disc.

Computerized methods and systems for estimating a dual-energy X-ray absorptiometry (DEXA) score from CT imaging data by receiving imaging data of a computed tomography (CT) scan of a body of a patient containing at least a bone portion, segmenting the bone portion from the imaging data , computing at least one grade based on pixel associated values from the bone portion, and correlating the at least one grade with at least one score representing a relation to bone density values in a population obtained based on a DEXA scan. The grade is computed from a calculation of sub-grades performed for each one or a set of pixels having at least one of a common medial-lateral axial coordinate and a common cranial-caudal axial coordinate along a dorsal-ventral axis of a volume representation of the imaging data.