Methods and systems for image segmentation are provided. Image data may be acquired, wherein the image data may include a plurality of ribs. A rib region containing at least a portion of the plurality of ribs may be determined. At least one rib of the plurality of ribs may be selected as a target rib based on the rib region. At least one rib-probability-map relating to the target rib may be generated based on an artificial intelligence algorithm. A starting point of the target rib may be determined based on the image data, wherein the starting point may indicate a starting position for tracking the target rib. At least one portion of the target rib may be tracked based on the starting point and the at least one rib-probability-map. A segmented rib may be obtained by segmenting the at least one portion of the target rib.

A method for measuring an X-ray image of an area undergoing medical examination that has at least one object. A 3D model is provided of the area undergoing examination that includes a virtual 3D object to be assigned to the object to be measured, a digitally reconstructed X-ray picture is computed based on the 3D model and under the assumption of a virtual projection direction, the X-ray image is compared with the digitally reconstructed X-ray picture, the virtual projection direction is changed relative to the virtual 3D object. The steps of comparing and changing are repeated until a virtual projection direction with maximum correlation between the X-ray image and the digitally reconstructed X-ray picture is found. An object plane is determined that is to be assigned to the virtual 3D object. A corrected projection direction is defined and the X-ray image is measured.

An assistive apparatus for organ localization, includes storing a 3D representation and CT images of an anatomical portion of the body of a subject. A localization circuitry determines a rib region and a spine region in the CT images and calculates first and second number of voxels within a first and second region of the 3D representation, respectively. The localization circuitry determines the right side of the body in the CT images, based on a comparison result for the first and second number of voxels. The localization circuitry detects a first bottom portion of right lung based on a distribution of intensity values of pixels in a region of right lung. The localization circuitry detects a second bottom portion of the rib region and localizes the liver organ in the CT images, from a reference of the detected first bottom portion and the detected second bottom portion.

A medical image processing apparatus comprises processing circuitry configured to: obtain a three-dimensional (3D) image that is representative of an anatomical region of a subject; acquire a stream of two-dimensional images that are representative of the anatomical region of the subject; set a first rendering direction by performing a 2D/3D registration procedure in respect of the 3D image and a 2D image of the stream of 2D images; generate a first rendered image from the 3D image based on the first rendering direction; and for each of a plurality of subsequent 2D images of the stream of 2D images, determine whether a condition is satisfied, the condition being dependent on at least one of a 2D misalignment and a time since last 2D/3D registration procedure; when the registration condition is satisfied, select one of the subsequent 2D images; reset the rendering direction to obtain a second rendering direction by performing a 2D/3D registration procedure in respect of the 3D image and the selected 2D image; and regenerate a second rendered image from the 3D image based on the second rendering direction.

Presented herein are systems and methods for registering one or more images of one or more subjects based on the automated generation of artificial landmarks. An artificial landmark is a point within an image that is associated with a specific physical location of the imaged region. The artificial landmarks are generated in an automated and robust fashion along the bones of a subject's skeleton that are represented in the image (e.g. graphically). The automatically generated artificial landmarks are used to correct distortion in a single image or to correct distortion in and/or co-register multiple images of a series of images (e.g. recorded at different time points). The artificial landmark generation approach described herein thereby facilitates analysis of images used, for example, for monitoring the progression of diseases such as pulmonary diseases.

A method of matching medical images according to user-defined matches rules. In one embodiment, the matched medical images are displayed according user-defined display rules such that the matched medical images may be visually compared in manner that is suitable to the viewer's viewing preferences.

Systems and methods are provided for an image processing system. In an example, a method includes acquiring a pathology dataset, acquiring a reference dataset, generating a deformation field by mapping points of a reference case of the reference dataset to points of a patient image of the pathology dataset, manipulating the deformation field, applying the deformation field to the reference case to generate a simulated pathology image including a simulated deformation pathology, and outputting the simulated pathology image.

A computer-implemented method of detecting and quantifying a spinal curve is disclosed herein. The method comprises obtaining an infrared radiometer camera, positioning the infrared radiometer camera for receiving thermal data for a spine of a subject, the camera being horizontally spaced about meters to about 3 meters from the spine, scanning at least a portion of the spine with the infrared radiometer camera to obtain the thermal data, analyzing the thermal data using machine learning software which uses a classification algorithm to determine the presence of the spinal curve, and calculating a first Cobb angle for the curve of the subject's spine. Corresponding systems and additional methods also are disclosed.

According to some embodiments, the process includes the steps of: a) taking a sagittal preoperative x-ray of the vertebral column of the patient to be treated, extending from the cervical vertebrae to the femoral heads; b) on that x-ray, identifying points on S1, S2, T12 et C7; c) depicting, on the said x-ray, curved segments beginning at the center of the plate of S1 et going to the center of the plate of C7; e) identifying, on that x-ray, the correction(s) to be made to the vertebral column, including the identification of posterior osteotomies to make; f) pivoting portions of said x-ray relative to other portions of that x-ray, according to osteotomies to be made; g) performing, on said x-ray, a displacement of the sagittal curvature segment extending over the vertebral segment to be corrected; h) from a straight vertebral rod (TV), producing the curvature of that rod according to the shape of said sagittal curvature segment in said displacement position.

An embodiment in accordance with the present invention provides a method for 3D-2D registration (for example, registration of a 3D CT image to a 2D radiograph) that permits deformable motion between structures defined in the 3D image based on a series of locally rigid transformations. This invention utilizes predefined annotations in 3D images (e.g., the location of anatomical features of interest) to perform multiple locally rigid registrations that yield improved accuracy in aligning structures that have undergone deformation between the acquisition of the 3D and 2D images (e.g., a preoperative CT compared to an intraoperative radiograph). The 3D image is divided into subregions that are masked according to the annotations, and the registration is computed simultaneously for each divided region by incorporating a volumetric masking method within the 3D-2D registration process.