A method of extracting and displaying postural measurements from patient data includes retrieving, by a processor of a computing device, the patient data from memory. The patient data includes a geometric mesh representation of a patient, including a plurality of data points corresponding to spatial coordinates of a plurality of vertices in three dimensions. The method also includes determining, by the processor, a reference geometry along the geometric mesh representation in a fixed position with respect to the spatial coordinates; determining, by the processor, a landmark corresponding to one of skeletal or soft tissue anatomy for the patient; and determining, by the processor, a postural deviation of a body portion of the patient by comparing the reference geometry and the landmark. The method further includes displaying, by a display of the computing device, a graphical user interface indicating a characteristic related to the postural deviation

Method and apparatus are provided for use with a procedure in which interventions are performed with respect to at least first and second vertebrae of a spine of a subject. Imaging data of the subject's spine is acquired using an imaging device. At least one computer processor is used to generate, upon a display, a spinal roadmap image of at least a portion of the spine that contains the first and second vertebra, automatically label vertebra within the spinal roadmap image, determine that an intervention has been performed with respect to the first vertebra, such that an appearance of the first vertebra has changed, and automatically update the spinal roadmap to reflect the change in the appearance of the first vertebra, such that the updated spinal roadmap is displayed while the intervention is performed with respect to the second vertebra. Other applications are also described.

An exemplary method of determining a surgical spinal correction for a subject using analysis of motion capture images of the subject, which uses the steps of obtaining pre-operative three-dimensional images of a spinal region, obtaining a pre-operative time sequenced set of images of the subject during a movement progression of said subject, calculating in a plurality of the motion capture images, alignment parameters relating to upper and lower body regions of the subject, and determining if any of the calculated alignment parameters are outside their predetermined acceptable ranges in one or more of the images, iteratively adjusting anatomical elements in three-dimensional images until all of the calculated alignment parameters are within their predetermined acceptable ranges; and adjusting spinal anatomy in the three-dimensional images according to the degree of adjustment of spinal parameters in the motion capture images to determine a surgical spinal correction.

Systems and methods for designing and implementing patient-specific surgical procedures and/or medical devices are disclosed. An example method includes obtaining, first multi-dimensional image data of an anatomical region of a patient in a first loading state; determining, from the first multi-dimensional image data, regions corresponding to anatomic elements of a spine; identifying, in each region, a first set landmarks; obtaining, a second multi-dimensional image data of the anatomical region comprising the spine of the patient in a second loading state; identifying a second set of landmarks from the second multi-dimensional image data that map to the first set of landmarks; obtaining an aligned multi-dimensional image data of the patient's spine; generating a design for a medical implant based on spinopelvic parameters measured from the aligned multi-dimensional image data; and causing a medical implant to be manufactured by sending the design for the medical implement to a manufacturing device.

A system and method for providing a guided workflow through a series of ultrasound image acquisitions with reference images updated based on a determined anatomical position is provided. The method includes acquiring and displaying, by an ultrasound system, at least one ultrasound image of an anatomy. The method includes determining, by at least one processor, a position of the anatomy depicted in the at least one ultrasound image. The method includes automatically selecting and displaying, by the at least one processor, reference images corresponding to pre-defined views of an ultrasound examination. An orientation of anatomy depicted in the selected and displayed reference images is based on the determined position of the anatomy. The method includes acquiring and displaying, by the ultrasound system, additional ultrasound images of the anatomy corresponding to the selected and displayed reference images. The additional ultrasound images include pre-defined views of the ultrasound examination.

A method is disclosed for spinal anatomy segmentation. In one example, the method includes combining a fully convolutional network with a residual neural network. The method also includes training the combined fully convolutional network with the residual neural network from end to end. The method also includes receiving at least one medical image of a spinal anatomy. The method also includes applying the fully convolutional network with the residual neural network to at least one medical image and segmenting at least one vertebral body from the at least one medical image of the spinal anatomy.

Disclosed are systems and methods for rapid generation of simulations of a patient's spinal morphology that enable pre-operative viewing of a patient's condition and to assist surgeons in determining the best corrective procedure and with any of the selection, augmentation or manufacture of spinal devices based on the patient specific simulated condition. The simulation is generated by morphing a generic spine model with a three-dimensional curve representation of the patient's particular spinal morphology derived from existing images of the patient's condition. Other anatomical structures in the patient's skeletal system are likewise simulated by morphing a generic normal skeletal model, as applicable, particularly those skeletal entities that are connected directly or indirectly to the spinal column.

Methods and systems for performing computer-assisted image-guided surgery, including robotically-assisted surgery. A method of displaying image data includes displaying image data of a patient on a handheld display device, tracking the handheld display device using a motion tracking system, and modifying the image data displayed in response to changes in the position and orientation of the handheld display device. Further embodiments include a sterile case for a handheld display device, display devices on a robotic arm, and methods and systems for performing image-guided surgery using multiple reference marker devices fixed to a patient.

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.

A computer-implemented method for fully-autonomous level identification of anatomical structures within a three-dimensional medical imagery, includes: receiving a set of medical scan images of the anatomical structures; processing the set to perform an autonomous semantic segmentation of anatomical components and to store segmentation results; processing segmentation results by removing the false positives, and smoothing 3D surfaces of the generated anatomical components; determining morphological and spatial relationships of the anatomical components; grouping the anatomical components to form separate levels based on the morphological and spatial relationships of the anatomical components; processing the set using a convolutional neural network to autonomously assign an initial level type; assigning the determined level type to each group of anatomical components by combining the determined morphological and spatial relationships with the determined initial level type; assigning an ordinal identifier to each group of anatomical components; and storing information about the assigned levels with their ordinal identifier.