A computer-implemented system: at least one processor communicably coupled to at least one nontransitory processor-readable storage medium storing processor-executable instructions or data receives segmentation learning data comprising a plurality of batches of labeled anatomical image sets, each image set comprising image data representative of a series of slices of a three-dimensional bony structure, and each image set including at least one label which identifies the region of a particular part of the bony structure depicted in each image of the image set, wherein the label indicates one of a plurality of classes indicating parts of the bone anatomy; trains a segmentation CNN, that is a fully convolutional neural network model with layer skip connections, to segment semantically at least one part of the bony structure utilizing the received segmentation learning data; and stores the trained segmentation CNN in at least one nontransitory processor-readable storage medium of the machine learning system.

For processing a medical image, medical image data representing a medical image of at least a portion of a vertebral column is received. The medical image data is processed to determine a plurality of positions within the image. Each of the plurality of positions corresponds to a position relating to a vertebral bone within the vertebral column. Data representing the plurality of positions is processed to determine a degree of deformity of at least one vertebral bone within the vertebral column.

Systems and methods are provided for processing X-ray images, wherein the methods are implemented as a software program product executable on a processing unit of the systems. Generally, an X-ray image is received by the system, the X-ray image being a projection image of a first object and a second object. The first and second objects are classified, and a respective 3D model of the objects is received. At the first object, a geometrical aspect like an axis or a line is determined, and at the second object, another geometrical aspect like a point is determined. Finally, a spatial relation between the first object and the second object is determined based on a 3D model of the first object, a 3D model of the second object, and the information that the point of the second object is located on the geometrical aspect of the first object.

Disclosed is a method, a computer readable storage medium and an apparatus for processing medical image data. Input medical image data is received at a data processing system, which is an artificial intelligence-based system. An identification process is performed at the data processing system on the input medical image data to identify a volume of interest within which an instance of a predetermined anatomical structure is located. First and second determination processes are performed at the data processing system to determine, respectively, first and second anatomical directions for the instance of the anatomical structure that are defined relative to the coordinate system of the input medical image data. Output data relating to the first and second anatomical directions is output from the data processing system.

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.

In an embodiment, a method includes acquiring a first image data set of the patient, via an X-ray apparatus, at a first time point during the operative intervention, the first image data set including the reference structure, the anatomical structure and the reference system between the reference structure and the anatomical structure; acquiring a second image data set of the patient at a second time point, the second image data set including at least the reference structure; registering the second image data set to the first image data set. As a result of the registering of the second image data set to the first image data set, a registered second image data set is determined. Finally, an embodiment of the method includes determining the validity of the reference system by a comparison of the registered second image data set with the first image data set.

A method of determining the imaging arm's optimal 3-dimensional position and orientation for taking images of a body implant or body structure such as vertebral body is provided. Test images of vertebral body of interest are initially taken by the user and are received by the imaging device. The test images typically include AP and lateral x-ray images of the vertebral body. From the test images, the vertebral body is segmented. A 3-dimensional model of the vertebral body is then aligned against the corresponding vertebral body in the test images. Based on the alignment, a 3-dimensional position and orientation of the imaging arm for taking optimal A-P and lateral x-ray images are determined based on the aligned 3-dimensional model. The present method eliminates the need to repeatedly take fluoro shots manually to find the optimum images to thereby reduce procedural time, x-ray exposure and procedure costs.

Methods and systems are provided for whole-body spine labeling. In one embodiment, a method comprises acquiring a non-functional image volume of a spine, acquiring a functional image volume of the spine, automatically labeling the non-functional image volume with spine labels, automatically correcting the geometric misalignments and registering the functional image volume, and propagating the spine labels to the functional image volume. In this way, the anatomical details of non-functional imaging volumes may be leveraged to improve clinical diagnoses based on functional imaging, such as diffusion weighted imaging (DWI).

A method for providing a foraminal stenosis ratio using 3-dimensional CT includes (a) transmitting a spine image of a patient to an information extracting unit by an image capturing unit in response to an input signal transmitted from an input unit; (b) extracting spine boundary information and neural foramen area information based on a pixel value of the spine image by the information extracting unit; (c) storing the spine boundary information and the neural foramen area information by an information storing unit; (d) calculating the foraminal stenosis ratio by using the spine image, the spine boundary information, and the neural foramen area information by an information calculating unit; and (e) outputting maximum neural foramen area information of the neural foramen area information, a neural foramen angle for the maximum neural foramen area information, and the foraminal stenosis ratio by an output unit.

A method of generating a quantitative characterization of injury presence and status of spinal cord tissue using an adaptive CNN system for use in diagnostic assessment, surgical planning, and therapeutic strategy comprises preprocessing for artifact correction of diffusion based, spinal cord MRI data, training an adaptive CNN system with healthy and abnormal (injured/pathologic) spinal cord images obtained by imaging a population of healthy, typically developed spinal cord subjects and subjects with spinal cord injury, evaluating a novel, diffusion-based MRI image for injury biomarkers using the adaptive CNN system, generating a three-dimensional predictive axonal damage map for quantitative characterization and visualization of the novel, diffusion-based MRI image, and transmitting the sets of healthy and injured spinal cord images back to a central database for continued improvement of the adaptive CNN system training. A system for defining a predictive spinal axonal damage map is also described.