Systems and methods for workflow management for labeling the subject anatomy are provided. The method comprises obtaining at least one localizer image of a subject anatomy using a low-resolution medical imaging device. The method further comprises labeling at least one anatomical point within the at least one localizer image. The method further comprises extracting using a machine learning module a mask of the at least one localizer image comprising the at least one anatomical point label. The method further comprises using the mask to label at least one anatomical point on a high-resolution image of the subject anatomy based on the at least one anatomical point within the localizer image.

Some embodiments of the present disclosure provide a pneumothorax detection method performed by a computing device. The method may comprise obtaining predicted pneumothorax information, predicted tube information, and a predicted spinal baseline with respect to an input image from a trained pneumothorax prediction model; determining at least one pneumothorax representative position for the predicted pneumothorax information and at least one tube representative position for the predicted tube information, in a prediction image in which the predicted pneumothorax information and the predicted tube information are displayed; dividing the prediction image into a first region and a second region by the predicted spinal baseline; and determining a region in which the at least one pneumothorax representative position and the at least one tube representative position exist among the first region and the second region.

Proposed are an apparatus and method for spinal surgery planning, the spinal surgery planning method including: acquiring a two-dimensional (2D) spinal image of a patient through a medical imaging apparatus; calculating a registration relationship between coordinates in an image space and coordinates in a surgical space by registering the image space for the spinal image and the surgical space where spinal surgery is performed on the patient; generating a virtual three-dimensional (3D) figure in the surgical space; projecting the 3D figure onto the spinal image based on the registration relationship; adjusting the 3D figure so that the 3D figure can correspond to a predetermined landmark on the spinal image; and setting an insertion position and insertion path of a spinal prosthesis based on the spinal image and the 3D figure.

The technology relates to generating a three-dimensional point cloud model of an anatomical structure. A computer accessible memory stores a three-dimensional array of data elements describing multiple anatomical features of a subject, each of the data elements having associated therewith positional data and a separate parameter value. A processor may be configured to identify any data elements in the three-dimensional array having an associated parameter value satisfying a predefined threshold value associated with at least one anatomical feature. The processor may be further configured to generate a visually displayable three-dimensional point cloud model of at least one anatomical structure having a first plurality of points in the point cloud model which define an exterior perimeter of the at least one anatomical structure and a second plurality points in the point cloud model which define at least one feature interior of the exterior perimeter of the at least one anatomical structure.

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, determining at least one spine label seed point on a non-functional image volume, automatically labeling the non-functional image volume with a plurality of spine labels based on the at least one spine label seed point, automatically correcting the geometric misalignments and registering the functional image volume, adjusting the plurality of spine labels and propagating the adjusted 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).

In certain embodiments, the systems, apparatus, and methods disclosed herein relate to robotic surgical systems with built-in navigation capability for patient position tracking and surgical instrument guidance during a surgical procedure, without the need for a separate navigation system. Robotic based navigation of surgical instruments during surgical procedures allows for easy registration and operative volume identification and tracking. The systems, apparatus, and methods herein allow re-registration, model updates, and operative volumes to be performed intra-operatively with minimal disruption to the surgical workflow. In certain embodiments, navigational assistance can be provided to a surgeon by displaying a surgical instrument’s position relative to a patient’s anatomy. Additionally, by revising pre-operatively defined data such as operative volumes, patient-robot orientation relationships, and anatomical models of the patient, a higher degree of precision and lower risk of complications and serious medical error can be achieved.

A computer-implemented method for visualization of an elongated anatomical structure (20), for example of a fetal spine using ultrasound is provided. The method comprising the steps of: receiving a plurality of 3D ultrasound image volumes, each image volume depicting at least a portion of an elongated anatomical structure (20); on each 3D ultrasound image volume, automatically or semi-automatically fitting a parametric curve (30) to the depicted portion of the elongated anatomical structure, the parametric curve being defined by curve parameters; reformatting each 3D ultrasound image volume by applying a transformation which straightens the parametric curve along at least one axis, so as to generate a plurality of reformatted image volumes and reformatted parametric curves (32, 34); registering the reformatted image volumes with one another by determining the joining point of their respective parametric curves; and fusing the reformatted image volumes with one another to yield a fused image depicting the whole elongated anatomical structure or a larger portion thereof than the 3D ultrasound image volumes.

The present disclosure directs to a method for image processing. The method may include obtaining scanning data of a spine of a subject, determining one or more centrum parameters of each of a plurality of centrums of the spine based on the scanning data, and identifying at least one intervertebral disc based on the one or more centrum parameters.

Each of the at least one intervertebral disc may be between a pair of neighboring centrums of the plurality of centrums. The method may include determining an intervertebral disc reconstruction protocol of each of the at least one intervertebral disc, determining a target intervertebral disc of the at least one intervertebral disc, and reconstructing one or more images of the target intervertebral disc based on an intervertebral disc reconstruction protocol of the target intervertebral disc. The intervertebral disc reconstruction protocols may relate to MPR.

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.

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.