94 results about "Cone beam ct" patented technology

The invention provides a cone beam CT-based pelvic tumor adaptive radiotherapy dose prediction method and system, and is applied to the technical field of data processing. The method comprises the following steps: preprocessing a cone beam CT image of a target user to generate a target cone beam CT image; processing the target cone beam CT image and a preset CT image of the target user to generate preset target region sketching information; processing the physiological motion parameter information of the target user and the target physiological structure information of the target user to generate a dynamic dose influence factor; processing the initial image recognition processing model based on the training sample set to generate a target image recognition processing model; processing the target cone beam CT image and the preset target region sketching information based on a target image recognition processing model to generate a target CT image; and processing the target CT image, the dynamic dose influence factor and the physiological state information of the target user based on the target image recognition processing model to generate target dose prediction information.

The invention discloses an intelligent eye control bonding system and method for an orthodontic appliance, and relates to the technical field of intelligent oral medical equipment. Comprising a pupil eye-control image positioning module, a bracket initial coordinate is generated through doctor pupil trajectory tracking and doctor head dynamic compensation, a patient three-dimensional oral cavity model is combined for mapping, a three-dimensional visual interface is constructed by integrating oral scanning instrument and cone beam CT data, and the three-dimensional visual interface is projected to an entity tooth surface through a laser positioning projection device. And the dynamic tracking module corrects the posture of the bracket in real time through rigid compensation and elastic compensation based on inertial sensing data. The path planning and control module adopts an improved RRT *-Connect algorithm, and an elastic map and an impedance-admittance hybrid control strategy are fused to optimize a motion path. And the mechanical arm execution module adopts a modular six-axis framework, integrates a triple-combination driving system and a double-cavity end effector, and completes submillimeter-level bonding operation in an oral cavity limited space. The positioning precision and the bonding efficiency of the bracket are improved.

The invention discloses a jaw cystic lesion recognition method, device and equipment, a medium and a product, and relates to the field of jaw cystic lesions. The method comprises the following steps: marking tooth and jaw regions in an oral cavity cone beam CT image, and selecting a region of interest to obtain an interested marked image; labeling cyst category labels generated by jaw cystic lesions in the interested labeling images, and forming a training set; constructing a deep learning model introducing an attention mechanism; the attention mechanism introduces relative positions of cysts and teeth as interested features; training a deep learning model by using the training set to obtain a jaw cystic lesion recognition model; and marking tooth and jaw regions in a to-be-identified oral cavity cone beam CT image of the patient with the jaw cystic lesion, inputting the marked image of interest after marking the tooth and jaw regions into the jaw cystic lesion identification model, and outputting the cyst category of the patient with the jaw cystic lesion. The accuracy and efficiency of jaw cystic lesion diagnosis can be improved.

A program causes a computer to perform processing of: acquiring an endoscopic image obtained by capturing a subject using an endoscope; extracting region-of-interest information from the acquired endoscopic image; acquiring a three-dimensional medical image obtained by capturing an inside of a body of the subject using at least one of X-ray CT, X-ray cone beam CT, MRI-CT, and an ultrasonic diagnostic device; deriving position information in a coordinate system of the three-dimensional medical image specified by the region-of-interest information and the three-dimensional medical image; and storing the region-of-interest information and the three-dimensional medical image in association with each other based on the derived position information and a capturing time point of each of the endoscopic image and the three-dimensional medical image.

The invention discloses a cone beam CT full respiratory cycle image reconstruction method based on a four-dimensional neural network. The method comprises the following steps: step 1, acquiring cone beam projection data and a respiratory signal in a respiratory state; 2, a respiratory cycle is divided into N phases, multi-phase cone beam projection data Qi are obtained, i = 1, 2,..., N, the Qi is reconstructed through a filtered back projection algorithm, and a multi-phase under-sampling CT reconstructed image # imgabs0 # is obtained; 3, # imgabs1 # is input into a four-dimensional neural network, and a three-dimensional deformation field Di is obtained; 4, performing weighted summation after deformation on the # imgabs2 # by using the Di to obtain a reference phase reconstruction image Xref after motion correction; and step 5, performing inversion on the Di to obtain a three-dimensional reverse deformation field # imgabs3 #, for any breathing phase, performing image deformation on the reference phase reconstruction image Xref by using the corresponding # imgabs4 #, and performing reconstruction to obtain a motion correction image Xi. According to the method, the breathing movement correction can be quickly carried out on the cone beam CT image.

The invention relates to a cone beam CT imaging scanning parameter determination method and imaging equipment, and the method comprises the steps: designing an optimization model for a microgravity environment, carrying out the optimization processing of the cone beam CT imaging scanning parameters based on a pre-constructed optimization model with the maximum imaging performance as a target, and obtaining the optimal parameters of the cone beam CT imaging. Wherein optimization problem modeling and solving are sequentially carried out in a projection domain and an image domain so as to obtain target scanning parameters meeting constraint conditions, and according to the technical scheme of the invention, aiming at factors such as scanning space and energy consumption in space, requirements of high spatial resolution, compact physical size of equipment, high image quality and low scanning dose are met, and the method and the device have the advantages of being high in efficiency and high in reliability. The method is applied to cone beam CT imaging in the space microgravity environment, and the comprehensive requirements of scientific research, health guarantee and medical service in the space environment are met.

There is provided a program causing a computer to execute processing including: acquiring an endoscopic image of a subject from an endoscope; acquiring a three-dimensional medical image obtained by capturing an image of an internal body portion of the subject by means of X-ray CT, X-ray cone beam CT, MRI-CT, or an ultrasonic diagnosis apparatus configured to capture a three-dimensional image of an internal body portion of the subject; generating a virtual endoscopic image reconfigured from the three-dimensional medical image, based on the acquired endoscopic image; calculating distance image information in the endoscopic image based on the virtual endoscopic image and the endoscopic image; and outputting operation assistance information on an operation of the endoscope based on the distance image information and the three-dimensional medical image.

The invention provides a cervical cancer self-adaptive radiotherapy assessment decision method and device, and relates to the technical field of medical artificial intelligence, and the method comprises the steps: obtaining a cone beam CT image and a plan CT image before radiotherapy of a patient to be subjected to radiotherapy on the day of radiotherapy, and extracting the structural quantitative features of the images; the method comprises the following steps: calculating a difference value of structured quantitative characteristics between a cone beam CT image on the day of radiotherapy and a plan CT image before radiotherapy to obtain multi-dimensional geometric change characteristics; and inputting the multi-dimensional geometric change features, the vectorized representation of the predetermined key region of the cone beam CT image and the clinical text information into a pre-trained clinical large language model, and outputting a self-adaptive radiotherapy demand tag. The problem that the prior art depends on manual analysis, and it is difficult to compare the change characteristics of the CT image between the radiotherapy day and the plan before radiotherapy to automatically analyze whether self-adaptive radiotherapy is executed on the radiotherapy day is solved.

The invention discloses a orthognathic surgery robot system, which comprises a multi-modal data fusion module, a cone beam CT (Computed Tomography) and intraoral scanning are integrated, a high-resolution three-dimensional bony structure image is acquired through the cone beam CT to reconstruct a three-dimensional anatomical model of maxillofacial skeletons, submillimeter details of tooth surfaces and occlusal relationships are captured through the intraoral scanning, and the submillimeter details of the tooth surfaces and occlusal relationships are acquired through the intraoral scanning; constructing a functional occlusion model; the navigation module is used for planning a navigation path through a preset self-adaptive path planning algorithm based on the three-dimensional anatomical model and the functional occlusion model of the maxillofacial skeleton of the target object; and the mechanical arm execution module is used for executing removal, osteotomy and fixation actions based on the navigation path. Through the multi-modal data fusion module, a personalized operation scheme can be formulated according to the specific anatomical structure and occlusal relationship of each patient.

The invention discloses a four-dimensional cone beam CT (Computed Tomography) reconstruction method based on an anatomical prior constraint diffusion model, which mainly solves the problem of poor quality of a reconstructed image in the prior art, and adopts the implementation scheme that the method comprises the following steps: acquiring CT original full-projection data of a batch of patients, and carrying out sparse sampling on the CT original full-projection data; performing data set division and reconstruction on the original full projection data and the sparse projection data to obtain a reference true value and a low-quality CT image containing a large number of artifacts; preliminarily enhancing the CT image containing a large number of artifacts by using the pre-trained ResViT network to obtain a CT image without artifacts; segmenting the CT image without artifacts by using a body segmentation network to obtain anatomical information of each organ; constructing a four-dimensional cone beam CT motion compensation reconstruction model based on anatomical prior constraints, and training and verifying the four-dimensional cone beam CT motion compensation reconstruction model; and inputting the tested data into the trained reconstruction diffusion model to obtain a reconstruction result. According to the method, artifacts of sparse cone beam CT in a specific organ area can be thoroughly eliminated, the reconstructed image is clear in details, and the method can be used for tracking and positioning tumor movement caused by respiration.

The invention provides a proton helium ion treatment device. The proton helium ion treatment device comprises an injector, an accelerator, a rotating rack, a treatment head, a beam distribution system and a six-dimensional treatment bed which are sequentially arranged in the conveying direction of a beam. The injector comprises a hydrogen ion source and a helium ion source which can be switched; the treatment head and beam distribution system comprises an integrated scanning magnet, a position ionization chamber, a dose ionization chamber, a ridge-shaped filter and a range shifter which are installed on a treatment head support and sequentially arranged in the beam conveying direction, and the integrated scanning magnet is used for beam deflection in the transverse X direction and the transverse Y direction at the same time. A cone beam CT system is further fixedly installed on the rotating rack, and precise positioning and positioning correction of a patient are achieved in combination with the six-dimensional mechanical arm treatment bed. The proton helium ion treatment device can meet the requirement for high precision of patient positioning in ion treatment, ion treatment with optional proton helium ions can be achieved, and the treatment system is more compact through the design of the integrated scanning magnet.

The invention discloses a four-dimensional dynamic image guided dose optimization system for tumor radiotherapy, and belongs to the technical field of medical imaging and radiotherapy. The system comprises a real-time image acquisition module, a dynamic anatomical model construction module, an organ deformation prediction module, a motion compensation engine, a four-dimensional dose accumulation calculation module, a self-adaptive treatment plan generation module and a dose delivery control module. A patient-specific four-dimensional dynamic anatomical model is constructed based on a deep neural network, an autoregression algorithm is adopted to predict organ motion trails, motion compensation parameters are calculated in real time, cumulative dose distribution considering tissue deformation is accurately calculated, a treatment plan is automatically adjusted, and dose delivery with millimeter-level precision is achieved. According to the system, the target area coverage deviation is reduced by 62%, the normal tissue irradiation dose is reduced by 37%, and the precision and safety of thoracic and abdominal tumor radiotherapy are remarkably improved.

Based on the traditional calibration method, a phantom method for calibrating the rotation center and SOD of the cone beam CT with high precision is provided, which is simple and efficient to improve the calibration precision of the parameters, the CT geometric parameters are calibrated by using the traditional method initially, after the calibration is completed, the steel needle phantom is used for CT scanning, image reconstruction, and the reconstructed image is post-processed, the rotation center and the distance from the ray source to the rotation center (SOD parameter) are fine-tuned, so that the imaging section of the steel needle obtained is small, the steel needle section characteristics are met, the center area is not distorted, and the distance between the steel needles meets the actual steel needle spacing.

The present invention relates to an apparatus (10) for estimation of Hounsfield unit values for voxels of a 3D cone beam CT image, the apparatus comprising: —a processing unit (20); and —an output unit (30). A 3D cone beam CT image comprises voxels with associated attenuation coefficient values. The processing unit is configured to calculate a histogram of at least some of the voxels of the 3D cone beam CT image representing a number of voxels having specific attenuation coefficient values or specific ranges of attenuation coefficient values. The processing unit is configured to identify a specific attenuation coefficient value or specific range of attenuation coefficient values associated with a first feature representative of a part of the 3D cone beam CT image, wherein the first feature is air or soft tissue or bone, and wherein the identification comprises utilization of the histogram. The processing unit is configured to determine an attenuation value to Hounsfield Unit converter, wherein the determination of the attenuation value to Hounsfield Unit converter comprises utilization of a correlation between the specific attenuation coefficient value or specific range of attenuation coefficient values associated with the first feature with a Hounsfield Unit for the first feature. The processing unit is configured to transform attenuation coefficient values of voxels in the 3D cone beam CT image to Hounsfield Units based on the attenuation value to Hounsfield Unit converter. The output unit is configured to output information relating to Hounsfield units of voxels of the 3D cone beam CT image.

The present invention relates to a device (10) for estimating Henry unit values for voxels of a 3D cone beam CT image, the device comprising: a processing unit (20); and an output unit (30). The 3D cone beam CT image includes voxels having associated attenuation coefficient values. The processing unit is configured to calculate a histogram of at least some of the voxels of the 3D cone beam CT image, the histogram representing a number of voxels having a particular attenuation coefficient value or a particular range of attenuation coefficient values. The processing unit is configured to identify a particular attenuation coefficient value or a particular range of attenuation coefficient values associated with a first feature representing a portion of the 3D cone beam CT image, where the first feature is air or soft tissue or bone, and where the identifying includes utilizing a histogram. The processing unit is configured to determine an attenuation value to a Hensi unit converter, where the determination of the attenuation value to the Hensi unit converter includes utilizing a correlation between a particular attenuation coefficient value or a particular attenuation coefficient value range associated with the first feature and a Hensi unit for the first feature. The processing unit is configured to transform attenuation coefficient values of voxels in the 3D cone beam CT image to Hunsler units based on the attenuation value-to-Hunsler unit converter. The output unit is configured to output information related to a Hensz unit of a voxel of the 3D cone beam CT image.

The application relates to a correction phantom and a correction method of a cone beam CT, and the method comprises the following steps: adjusting the pose of a correction phantom to obtain a first projection of the correction phantom; moving a detector by a first distance to obtain a second projection of the correction phantom; rotating the correction phantom by 180 degrees to obtain a third projection of the correction phantom; calculating geometric parameters according to the first projection, the second projection and the third projection; and correcting the cone beam CT according to the geometric parameters. The design of the correction phantom solves the CT system geometric deviation and the calculation error generated by placing the correction phantom in the prior art. The pose of the correction phantom is adjusted to adjust the imaging angle of the light beam and the phantom according to the projection shape of the first hole and the second hole, so that the calculation precision is improved. The multiple projections obtained by adjusting the correction phantom, the data obtained by different projections and the independent calculation of each geometric parameter can avoid the introduction of accumulated errors, and the precision of the geometric parameters is effectively improved.

The invention discloses an image reconstruction method, electronic equipment, a storage medium and a product, relates to the technical field of medical treatment, and is used for optimizing an image reconstruction effect and reducing the influence of shielding artifacts. The method comprises the following steps: acquiring a cone beam CT projection image shielded by a light beam blocking array; light beam blocking substances on the light beam blocking array are asymmetrically arranged about the first axis; the first axis is parallel to a rotating shaft of the cone beam CT and passes through a beam center in a cone beam CT projection image; reconstructing the cone beam CT projection image based on the symmetric projection points and the shadow points in the cone beam CT projection image to obtain a reconstructed image; the symmetric projection points are symmetric points of shadow points in the cone beam CT projection image.

The invention relates to a head positioning device and an oral cavity cone beam CT system, and relates to the technical field of medical instruments. The device comprises a forehead structure used for supporting the forehead of a patient and serving as a reference for connecting the device with an external frame; the supporting structure is connected to the forehead structure and used for providing vertical supporting and adjusting functions; the locking structure is mounted at the top of the supporting structure and used for applying constraining force to the head of the patient from the upper portion or the side portion; wherein the forehead gear structure, the supporting structure and the locking structure work together to achieve adjustable fixing of the head of a patient. Through cooperative work of the three parts of structures, multi-dimensional adjustment and precise fixation of the head of a patient can be achieved, pressure is effectively dispersed, local indentation and discomfort are avoided, meanwhile, the stability of a positioning reference is ensured, and the image quality is improved.

The invention provides a body surface mark assisted temporomandibular joint cone beam CT scanning positioning method. The method comprises the following steps: acquiring temporomandibular joint large-view cone beam CT image data of a plurality of testees; based on the CT image data, performing three-dimensional reconstruction and head position correction to obtain head projection views corresponding to a plurality of testees; based on body surface mark points and temporal-mandibular joint bone mark points in a plurality of head projection views, the moving distance of a positioning line of large-view or small-view cone-beam CT scanning relative to the body surface mark points in a corresponding coordinate axis is obtained through statistics, and cone-beam CT scanning is positioned based on the moving distance. And positioning the positioning line of the large-view or small-view cone beam CT scanning to the corresponding optimal position. The problems that in the prior art, due to the fact that an accurate and rapid positioning mode is lacked during cone beam CT scanning positioning, shooting time is long, working efficiency is low, and the shooting failure probability caused by double artifacts due to movement of a patient is increased are solved.

The invention relates to a geometric parameter estimation method for cone-beam CT, belongs to the technical field of cone-beam computed tomography, and solves the problems of high cost and low efficiency of a technical scheme for determining geometric parameters of cone-beam CT in the prior art. The geometric parameter estimation method comprises the following steps: extracting same preset sampling lines from projection images at all projection angles to obtain sinograms corresponding to all the preset sampling lines; determining a first transverse offset of each preset sampling line according to the sinogram corresponding to each preset sampling line, and performing linear fitting based on the first transverse offset of each preset sampling line to obtain a torsion angle of the cone beam CT; and performing rotation correction on the projection images at each projection angle by using the torsion angle of the cone beam CT, extracting a sinogram corresponding to the center line from the projection images at each projection angle after rotation correction, and taking the transverse offset of the center line as the transverse offset estimation value of the cone beam CT. And the geometric parameters of the cone beam CT can be quickly estimated.

The aim is to improve backprojection from cone-beam CT projection images. To this end, a method for obtaining a fluoroscopic image from cone-beam CT projection images is proposed, in which the fluoroscopic image is obtained by weighted backprojection. In the backprojection, projection weights are used that depend on the position of a pixel in the image space.

The present invention provides a method of matching data, which is performed by an apparatus for matching data, the method including synchronizing acquisition time points of cone beam computed tomography (CBCT) data and 3D scan data, conforming coordinate systems of the CBCT data and the 3D scan data, constructing a 3D scan image in which a motion is corrected by matching depth maps created each time the 3D scan data are acquired, and constructing a CBCT 3D image in which the motion is corrected from the CBCT data by applying motion information acquired by matching the depth maps.

The embodiment of the invention relates to the field of cone-beam CT imaging, and discloses a large-view-field cone-beam CT imaging method, device and equipment and a storage medium. A target is scanned around a scanning center according to a first sub-scanning path and a second sub-scanning path; in the scanning process, the main mechanical arm and the auxiliary mechanical arm drive the transmitting device and the receiving device to synchronously move, so that the center of the conical imaging beam is always perpendicular to the center of the receiving device; the first sub-scanning path comprises that the center of the transmitting device deflects for a preset angle from the center of the conical imaging beam to the scanning center to a preset direction and then rotates along a set track; the second sub-scanning path comprises that the center of the transmitting device rotates in the reverse direction of the set track after deflecting by two times of a preset angle in the reverse direction of the preset direction at the ending position of the first sub-scanning path; scanning data at the same position are combined and then subjected to back projection reconstruction, X-ray energy can be efficiently utilized without changing hardware equipment, and large-view cone beam CT imaging is achieved.

The application relates to a method, device and medium suitable for multi-energy cone beam CT projection interpolation completion. The method comprises the following steps: acquiring low-energy projection and high-energy projection of a multi-energy cone beam CT energy spectrum imaging system; performing preliminary iterative sparse reconstruction and material decomposition on the low-energy projection and the high-energy projection respectively to obtain a base material one reconstructed image and a base material two reconstructed image; performing an image enhancement operation on the base material one reconstructed image and the base material two reconstructed image respectively, then performing forward projection, and performing weighted processing by using pre-calibrated high-low energy spectrum information to obtain low-energy complete projection data and high-energy complete projection data under full viewing angle. Therefore, the image domain sparse reconstructed image is used to guide the projection domain high-low energy projection data to perform energy spectrum interpolation completion, the problem of inconsistent high-low energy projection angles of a fast kilovoltage switching energy spectrum CBCT is solved, and the image quality and material decomposition accuracy are greatly improved.

The present invention provides a multifunctional gamma knife treatment device with cone-beam CT detection. The multifunctional gamma knife treatment device with cone-beam CT detection is characterized in that the multifunctional gamma knife treatment device includes a cone-beam CT detection device and a gamma knife treatment device. The cone-beam CT detection device is arranged in front of the gamma knife treatment device. The cone-beam CT detection device includes a cone-beam CT detection frame, a head-body dual-purpose imaging tube device, and an imaging device detector. The head-body dual-purpose imaging tube device and the imaging device detector are symmetrically arranged on the cone-beam CT detection frame. The cone-beam CT detection frame includes a swivel support mechanism and a frame flip mechanism. The swivel support mechanism is flip-connected to the frame flip mechanism. By arranging the cone-beam CT detection device in front of the gamma knife treatment device and utilizing the flipping of the cone-beam CT detection device, both cone-beam CT detection and gamma knife treatment can be performed, thereby achieving multifunctional combined treatment and detection.

The present invention provides a method of matching data, which is performed by an apparatus for matching data, the method including synchronizing acquisition time points of cone beam computed tomography (CBCT) data and 3D scan data, conforming coordinate systems of the CBCT data and the 3D scan data, constructing a 3D scan image in which a motion is corrected by matching depth maps created each time the 3D scan data are acquired, and constructing a CBCT 3D image in which the motion is corrected from the CBCT data by applying motion information acquired by matching the depth maps.

The application provides an orthodontic tooth movement monitoring method and system based on wax occlusion records, which comprises the following steps: obtaining an intraoral scanning model and a cone beam CT tooth model at time T0, superimposing the two models to obtain a complete model of each tooth containing a tooth crown and a tooth root; obtaining a wax occlusion record at time T N , optically scanning the wax occlusion record to obtain a three-dimensional digital model of the occlusion record and uploading the three-dimensional digital model; matching the occlusion pits of the digital model of the occlusion record with the occlusal surfaces of the complete model of each tooth to restore a tooth position model at time T N ; superimposing the tooth position models at times T0 and T N , and establishing a local three-dimensional coordinate system for each tooth; calculating three-dimensional movement data of the teeth; and a doctor remotely monitors the tooth movement of a patient with high accuracy by obtaining accurate three-dimensional data of the tooth position changes, provides reliable data for subsequent correction, and guides the next treatment of the patient.

The invention provides a glioma image segmentation method and system based on multi-feature fusion, and relates to the technical field of medical image processing, and the method comprises the steps: carrying out the preprocessing of multi-modal image data, and obtaining the spatial alignment multi-modal image data; based on the spatially aligned multi-modal image data, respectively extracting texture and metabolism characteristics of a preoperative image, gray gradient and morphological characteristics of an intra-operative cone beam CT image and micro texture characteristics of an intra-operative optical coherence tomography image; fusing the morphological characteristics of the intra-operative cone beam CT image and the microscopic texture characteristics of the intra-operative optical coherence tomography image, and constructing a multi-modal spatial characteristic field; based on the multi-modal spatial feature field, determining a spatial assessment origin, a main assessment axis and an auxiliary assessment axis, and defining a dynamic spatial assessment sector accordingly; and arranging a series of spatial feature observation points in the dynamic space evaluation sector. According to the method, feature reliability quantification can be realized, so that adaptive precise feature fusion is completed, and the glioma subregion segmentation precision is improved.

The invention discloses a cone-beam CT arterial phase automatic scanning method and system based on forward projection, and relates to the technical field of medical image.The method comprises the steps that low-dose cone-beam CT pre-scanning is conducted on a target area when a contrast agent is not injected, a three-dimensional image is reconstructed, a to-be-monitored anatomical structure is selected on the three-dimensional image to form a three-dimensional area of interest, and the three-dimensional area of interest is obtained; the method comprises the following steps of: performing forward projection on a three-dimensional region of interest based on cone beam CT geometric parameters to obtain a two-dimensional monitoring region template corresponding to each rack angle, acquiring a real-time perspective image sequence in a contrast agent injection process, calling the corresponding two-dimensional monitoring region template for each frame of image to calculate a monitoring signal, acquiring a baseline value before contrast, calculating a difference value, and acquiring a real-time perspective image sequence; when the difference value reaches a preset threshold value, a trigger signal is output to start arterial phase cone beam CT scanning. Through three-dimensional pre-scanning targeted forward projection and real-time threshold monitoring, the arrival of a target focus contrast agent is accurately judged, the accuracy of the imaging opportunity in the arterial phase is improved, and the radiation dose is reduced.

System and method of more efficiently identifying and segmenting anatomical structures from 2-D cone beam CT images, rather than from reconstructed 3-D volume data, is disclosed. An image processing system receives, from a cone beam CT device, at least one 2-D x-ray image, which is part of a set of x-ray images taken from a 360 degree scan of a patient with a cone beam CT imaging device. The x-ray image contains at least one anatomical structure such as vertebral bodies to be segmented. The received x-ray is then analyzed in order to identify and segment the anatomical structure contained in the x-ray image based on a stored model of anatomical structures. Once the 360 degree spin is completed, a 3-D image volume from the x-ray image set is created. The identification and segmentation information derived from the x-ray image is then added to the created 3-D image volume.