Systems and methods for providing assistance to a surgeon during an implant surgery are disclosed. A method includes defining areas of interest in diagnostic data of a patient and defining a screw bone type based on the surgeon's input. Post defining the areas of interest, salient points are determined for the areas of interest. Successively, an XZ angle, an XY angle, and a position entry point for a screw are determined based on the salient points of the areas of interest. Successively, a maximum screw diameter and a length of the screw are determined based on the salient points. Thereafter, the screw is identified and suggested to the surgeon for usage during the implant surgery.

A borderline extraction element 15 extracts a borderline B of adjacent vertebrae from an X-ray image on which multiple vertebrae are projected connected in a line. A vertebral area setting element 17 sets a area sandwiched by the adjacent borderlines B as a vertebral area and an area number is put to the respective vertebral areas L. When the vertebral areas are erroneously set up, a setup data erase element 21 erases the data of the vertebral areas L and the area number when the borderline is corrected. The setup data are erased, so that the borderline B can be shift-corrected to any location. A vertebral area setting element 17 resets the vertebral area L based on the location of the corrected borderline. Accordingly, the respective vertebral areas L are set up to the accurate locations and only the borderline B extracted to the wrong location is shift-corrected and the vertebral areas are reset. The work-burden to the operator for setting the vertebral area can be largely lessened.

The present disclosure relates to an image processing method. The method may include: obtaining image data; reconstructing an image based on the image data, the image including one or more first edges; obtaining a model, the model including one or more second edges corresponding to the one or more first edges; matching the model and the image; and adjusting the one or more second edges of the model based on the one or more first edges.

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.

A method of assessing spinal column stability involves receiving image data corresponding to a spinal column of a patient; determining, based on the image data, a material strength of bony anatomy in at least a portion of the spinal column; completing a first stability assessment of the spinal column, based at least in part on the determined material strength; modifying the image data to simulate removal of bony anatomy or soft tissue from the spinal column to yield modified image data; and completing a second stability assessment of the spinal column, based at least in part on the determined material strength and the modified image data.

A system for surgical planning and assessment of spinal pathology or spinal deformity correction in a subject, the system comprises a control unit configured to align one or more vertebral bodies of a biomechanical model to one or more vertebral bodies of the radiograph. The control unit is configured to receive one or more spinal correction inputs. The control unit is configured to, based on the received one or more spinal correction inputs, simulate the biomechanical model in a predetermined posture. The control unit is configured to provide for display one or more characteristics of the simulated biomechanical model.

An ultrasound probe for percutaneous insertion into an incision and related methods are disclosed herein, e.g., for imaging neural structures at a surgical site of a patient. An exemplary ultrasound probe can be a portable ultrasound probe configured to be passed percutaneously into an incision and can have an imaging region extending distally from a distal tip of the probe. In one embodiment the ultrasound probe can be a navigated portable ultrasound probe. The ultrasound probe can be connected to a computing station and configured to transmit images to the computing station for processing. In another embodiment, an ultrasound probe can be part of a network of sensors, including at least one external sensor, where the network of sensors is configured to transmit images to the computing station for processing. The computing station can process and display images to visualize and/or highlight neurological structures in an imaged region.

Diagnostic systems and methods for measuring and assessing spine instability are described which involve reconstruction of a dynamic three-dimensional model of a patient's spine moving through a range of motions, and optimization of the three-dimensional model to provide relative three-dimensional position and orientation data for each vertebra in the spine throughout the motion. Vertebral movement is thereby accurately measured and instability determined for presentation in a user-friendly form.

A method and system for image processing is provided. The technique includes acquiring data related to the processing, performing a pre-processing of the data, performing a segmentation of a subject, performing a post-processing of the result of the segmentation and managing storage of the data. The post-process includes a calibrating and a rendering of the data.

A method of a measuring kinematic parameter in a subject is provided. The method includes obtaining a first magnetic resonance (MR) image set of a bone marrow segment of the subject in a first position and obtaining a second MR image set of the bone marrow segment of the subject in a second position where the second position different from the first position. The method further includes registering the first image set with the second image set and measuring a kinematic parameter.