A method and system for improving spinal alignment parameters of a subject, by planning the shape of an intervertebral rod for attaching to previously implanted hardware whose positions and orientations are known from intraoperative images. Once the planned shape of the rod has been prepared, determining if, when attached to the inserted hardware, a spine configuration is achieved having acceptable values of selected spinal alignment parameters. If not, the shape of the rod is amended iteratively, until the selected spinal alignment parameters have acceptable values with attachability to the implanted hardware. If, after a predetermined number of iterations, the amended shape of the rod still does not achieve acceptable values of spinal alignment parameters, while maintaining attachability to the implanted hardware, performing a spinal manipulation procedure on at least one vertebra of the spine to increase the attachability of the rod to the implanted hardware.

Described herein is a novel method and system for segmentation of the spine using 3D volumetric data. In embodiments, a method includes an extracting step, localization step, and segmentation step. The extracting step comprises detecting the spine centerline and the spine canal centerline. The localization step comprises localizing the vertebra and intervertebral disc centers. Background and foreground constraints are created for each vertebra digit. Segmentation is performed for each vertebra digit and based on the hard constraints.

A spinal image generation system based on the ultrasonic rubbing technique, comprises an acquisition unit and a processing unit. The system generates the ultrasonic rubbing based on two-dimensional spinal ultrasonic images. The image needs to include surface characteristic contour of the vertebra structure. The ultrasonic rubbing matches with a digital medical image through characteristic contour. After matching, a personalized spinal surface topographical map is established, which keeps real-time updating consistently with the intraoperative posture of the patient under surgical condition. A positioning and navigation system for spinal surgery based on the spinal image generation system, comprising a navigation module and the image generation system above. The navigation system can acquire a personalized spinal surface topographical map, which keeps real-time updating consistently with the intraoperative posture of the patient under surgical condition.

Systems, instruments, and methods for medical treatment are disclosed. The methods comprise, by a computing device: receiving information identifying at least one first point on a body part shown in a medical image; overlaying a first mark on the medical image for the at least one first point; generating a spline based at least on the first mark; overlaying a second mark for the spline on the medical image; identifying a location of at least one second point on the body part shown in the medical image based on the first and second marks; overlaying a third mark for the at least one second point on the medical image; and using at least the third mark to facilitate the medical treatment of an individual whose body part is shown in the medical image.

A separation unit that generates a separated image in which a plurality of structures are separated, from an image including the plurality of structures receives an input of an image pair that includes a target image relating to at least a part of the plurality of structures and a non-separation image not including the structure, to output a separation image in which one of the structures is extracted from the target image. The separation unit receives an input of a new image pair including the target image and the separation image, to output a new separation image in which another one of the structures is extracted from the target image. The separation unit repeats the reception of the input of the new image pair including the target image and the new separation image and the output of a new separation image in which another one of the structures is extracted from the target image.

Conventionally, systems and methods have been provided for manual annotation of anatomical landmarks in digital radiography (DR) images. Embodiments of the present disclosure provides system and method for anatomical landmark detection and identification from DR images containing severe skeletal deformations. More specifically, motion artefacts and exposure are filtered from an input DR image to obtain a pre-processed DR image and probable/candidate anatomical landmarks comprised therein are identified. These probable candidate anatomical landmarks are assigned a score. A subset of the candidate anatomical landmarks (CALs) is selected as accurate anatomical landmarks based on comparison of the score with a pre-defined threshold performed by a trained classifier. Position of remaining CALs may be fine-tuned for classification thereof as accurate anatomical landmarks or missing anatomical landmarks. The CALs may be further fed to the system for checking misalignment of any of the CALs and correcting the misaligned CALs.

A method of modeling lungs of a patient includes acquiring computed tomography data of a patient's lungs, storing a software application within a memory associated with a computer, the computer having a processor configured to execute the software application, executing the software application to differentiate tissue located within the patient's lung using the acquired CT data, generate a 3-D model of the patient's lungs based on the acquired CT data and the differentiated tissue, apply a material property to each tissue of the differentiated tissue within the generated 3-D model, generate a mesh of the 3-D model of the patient's lungs, calculate a displacement of the patient's lungs in a collapsed state based on the material property applied to the differentiated tissue and the generated mesh of the generated 3-D model, and display a collapsed lung model of the patient's lungs based on the calculated displacement of the patient's lungs.

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.

Automated determination of muscle mass from images can be carried by performing a thresholding process to an image file to generate a contrasted image, and segmenting pixels of the contrasted image into bone and not bone. The system can distinguish muscle from organ for the pixels segmented as not bone by determining a location of a rib cage of the patient using the pixels segmented as bone, and removing pixels segmented as not bone that are located within the location of the rib cage. The system can calculate a volume of muscle based on remaining pixels segmented as not bone; calculate a total muscle mass based on the volume of muscle; and provide the total muscle mass of the patient. The total muscle mass of the patient can then be used for applications including calculating a glomerular filtration rate.

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.