A system for surgical planning and assessment of spinal deformity correction is provided that has a spinal imaging system and a control unit. The spinal imaging system is configured to collect at least one digitized position of one or more vertebral bodies of a subject. The control unit is configured to receive the at least one digitized position, and calculate, based on the at least one digitized position, an optimized posture for the subject. The control unit is configured to receive one or more simulated spinal correction inputs, and based on the inputs and optimized posture, predict an optimal simulated postoperative surgical correction.

A system for executing a three-dimensional (3D) intraoperative scan of a patient is disclosed. A 3D scanner controller projects the object points included onto a first image plane and the object points onto a second image plane. The 3D scanner controller determines first epipolar lines associated with the first image plane and second epipolar lines associated with the second image plane based on an epipolar plane that triangulates the object points included in the first 2D intraoperative image to the object points included in the second 2D intraoperative image. Each epipolar lines provides a depth of each object as projected onto the first image plane and the second image plane. The 3D scanner controller converts the first 2D intraoperative image and the second 2D intraoperative image to the 3D intraoperative scan of the patient based on the depth of each object point provided by each corresponding epipolar line.

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.

A diagnosis support system includes a calculator configured to calculate position information indicating a positional relationship between a biological sensor and a predetermined region of a measurement target; and an extractor configured to extract, from biological information already diagnosed, biological information that is associated with position information, which is similar to the position information calculated by the calculator.

A machine-based learning method estimates a probability of bone fractures in a 3D image, more specifically vertebral fractures. The method and system utilizing such method utilize a data-driven computational model to learn 3D image features for classifying vertebra fractures. A three-dimensional medical image analysis system for predicting a presence of a vertebral fracture in a subject includes a 3D image processor for receiving and processing 3D image data of a 3D image of the subject, producing two or more sets of 3D voxels. Each of the sets of 3D voxels corresponds to an entirety of the 3D image and each of the sets of 3D voxels consists of equal 3D voxels of different dimensions. The system also includes a voxel classifier for assigning the 3D voxels one or more class probabilities each of the 3D voxels contains a fracture using a computational model, and a fracture probability estimator for estimating a probability of the presence of a vertebral fracture in the subject.

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 method and system for detecting spinal stenosis is provided. The method may receive image data corresponding to a spine region of a patient. The method may also identify a spinal cord in the image data. The method may determine at least one compression of the spinal cord and may mark an anatomical element proximate to a location of the determined at least one compression to yield at least one marking. The method may generate a decompression plan based on the at least one marking.

Systems and methods are provided in which intraoperatively acquired surface data is employed to verify the correspondence of an intraoperatively selected spinal level with a spinal level that is pre-selected based on volumetric image data. Segmented surface data corresponding to the pre-selected spinal levels may be obtained from the volumetric image data, such that the segmented surface data corresponds to a spinal segment that is expected to be exposed and identified intraoperatively during the surgical procedure. The segmented surface data from the pre-selected spinal level, and adjacent segmented surface data from an adjacent spinal level that is adjacent to the pre-selected spinal level, is registered to the intraoperative surface data, and quality measures associated with the registration are obtained, thereby permitting an assessment or a determination of whether or not the pre-selected spinal surface (in the volumetric frame or reference) is likely to correspond to the intraoperatively selected spinal level.

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.

Methods and systems for image processing are provided. Image data may be obtained. The image data may include a plurality of voxels corresponding to a first plurality of ribs of an object. A first plurality of seed points may be identified for the first plurality of ribs. The first plurality of identified seed points may be labelled to obtain labelled seed points. A connected domain of a target rib of the first plurality of ribs may be determined based on at least one rib segmentation algorithm. A labelled target rib may be obtained by labelling, based on a hit-or-miss operation, the connected domain of the target rib, wherein the hit-or-miss operation may be performed using the labelled seed points to hit the connected domain of the target rib.