A method includes receiving a three-dimensional image dataset of a surgical site of a patient. The method also includes segmenting one or more anatomical features of the surgical site based on the three-dimensional image dataset. The method also includes receiving a two-dimensional image of the surgical site of the patient and registering the two-dimensional image to an image from the three-dimensional image dataset. The method also includes displaying a two-dimensional representation of the segmented one or more anatomical features based on the registered two-dimensional image and the image from the three-dimensional image dataset.

A method for analysis of spine anatomy and stenosis is disclosed herein. The method includes preprocessing (3203) of images and then running segmentation models for each area of interest such as the foramen, disc, canal, vertebra (3206). In image post processing (3207), the system runs various heuristics to ensure accuracy (3208), computes areas (3209), and then runs a comparison model (3210). The report template produces HTML that is then converted to a PDF file of the final report (3214).

The present disclosure includes systems, methods and media for rendering objects translucent and for recovery of anatomical information blocked by the objects in medical images.

Disclosed herein are systems and methods for registering a first three-dimensional medical image dataset taken with a first image capturing device with a second 3D dataset taken with a second image capturing device.

A method for use of machine learning in computer-assisted anatomical prediction. The method includes identifying with a processor parameters in a plurality of training images to generate a training dataset, the training dataset having data linking the parameters to respective training images, training at least one machine learning algorithm based on the parameters in the training dataset and validating the trained machine learning algorithm, identifying with the processor digitized points on a plurality of anatomical landmarks in a radiographic image of a person's skeleton displayed on a screen by determining anatomical relationships of adjacent bony structures as well as dimensions of at least a portion of a body of the skeleton in the displayed image using the validated machine learning algorithm and a scale factor for the displayed image, and making an anatomical prediction of the person's skeletal alignment based on the determined anatomical dimensions and a known morphological relationship.

A method for automatically determining spine deformation from an image showing a number of vertebrae of the spine, comprises: detecting center points of the number of vertebrae shown in the image; constructing a center line based on the center points; computing a local tilt at points along the center line; determining positive and negative tilt-maxima of the local tilt and selecting two reference vertebrae having center points closest to the positive and negative tilt-maxima; segmenting an upper endplate of the cranial reference vertebra; segmenting a lower endplate of the caudal reference vertebra; computing an angle between the upper endplate and the lower endplate; and outputting the angle.

A radiographic image display apparatus 3 included in a radiographic imaging system 100 includes a hardware processor that obtains an image data item on each of the frame images generated by the radiographic imaging apparatus 2, detects a situation of the subject at least at a time point in the dynamic imaging, associates the detected situation of the subject with the obtained image data items, and issues a specific output for notification that the subject is in a specific situation when the subject is in a state of a specific frame image fs, in a case where the display is caused to display the specific frame image fs, this specific frame image fs being taken when it is detected that the situation of the subject is the specific situation.

The present disclosure relates to the generation of partial surface models from volumetric datasets for subsequent registration of such partial surface models to surface topology datasets. Specifically, given an object that is imaged using surface topology imaging and another volumetric modality, the volumetric dataset is processed in combination with an approach viewpoint to generate one or more partial surfaces of the object that will be visible to the surface topology imaging system. This procedure can eliminate internal structures from the surfaces generated from volumetric datasets, thus increases the similarity of the dataset between the two different modalities, enabling improved and quicker registration.

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.

One or more example embodiments of the present invention relates in one aspect to a computer-implemented method includes receiving 2D topogram data of a patient; generating 2D topogram annotation data by applying a machine learning algorithm for topogram analysis onto the 2D topogram data; generating the medical imaging decision support data based on the 2D topogram annotation data; and providing the medical imaging decision support data.