The present application discloses a method, device, and system for processing a medical image. The method includes obtaining a source spinal image, identifying one or more vertebral bodies and one or more intervertebral discs comprised in the source spinal image, determining the vertebral body recognition results corresponding to the one or more vertebral bodies and the intervertebral disc recognition results corresponding to the one or more intervertebral discs, and determining target recognition results corresponding to the source spinal image based at least in part one on one or more of the vertebral body recognition results and the intervertebral disc recognition results.

Method of segmenting anatomical structures such as organs in 3D scans in an architecture that combines U-net, time-distributed convolutions and bidirectional convolutional LSTM.

Systems and methods are configured to extract images from provided source data files and to preprocess such images for content-based image analysis. An image analysis system applies one or more machine-learning based models for identifying specific features within analyzed images, and for determining one or more measurements based at least in part on the identified features. Such measurements may be embodied as absolute measurements for determining an absolute distance between features, or relative measurements for determining a relative relationship between features. The determined measurements are input into one or more machine-learning based models for determining a classification for the image.

Systems and methods are configured to extract images from provided source data files and to preprocess such images for content-based image analysis. An image analysis system applies one or more machine-learning based models for identifying specific features within analyzed images, and for determining one or more measurements based at least in part on the identified features. Such measurements may be embodied as absolute measurements for determining an absolute distance between features, or relative measurements for determining a relative relationship between features. The determined measurements are input into one or more machine-learning based models for determining a classification for the image.

Systems and methods are configured for preprocessing of images for further content based analysis thereof. Such images are extracted from a source data file, by standardizing individual pages within a source data file as image data files, and identifying whether the image satisfies applicable size-based criteria, applicable color-based criteria, and applicable content-based criteria, among others, utilizing one or more machine-learning based models. Various systems and methods may identify particular features within the extracted images to facilitate further image-based analysis based on the identified features.

Vertebrae of the spine in volumetric image are detected using multi-stage detection with trained artificial intelligence. In one embodiment, a trained neural network (116) is employed in a first stage to detect individual vertebra in sagittal images. Two-dimensional bounding boxes around the detected vertebrae are combined to generate a three-dimensional model of the spine. A panoramic image of the spine is generated based on the three-dimensional model to create a straightened view of the spine. The trained neural network is employed in a second stage to detect individual vertebra in the panoramic image. Two-dimensional bounding boxes around the detected vertebrae in the panoramic image are translated to three-dimensional space to create three-dimensional image data with three-dimensional bounding boxes.

A surgical navigation system includes: a 3D display system with a see-through visor; a tracking system comprising means for real-time tracking of: a surgeon's head, the see-through visor, a patient anatomy and a surgical instrument to provide current position and orientation data; a source of an operative plan, a patient anatomy data and a virtual surgical instrument model; a surgical navigation image generator configured to generate a surgical navigation image with a three-dimensional image representing simultaneously a virtual image of the surgical instrument corresponding to the current position and orientation of the surgical instrument and a virtual image of the surgical instrument indicating the suggested positions and orientation of the surgical instrument according to the operative plan data based on the current relative position and orientation of the surgeon's head, the see-through visor, the patient anatomy and the surgical instrument; wherein the 3D display system is configured to show the surgical navigation image at the see-through visor, such that an augmented reality image collocated with the patient anatomy in the surgical field underneath the see-through visor is visible to a viewer looking from above the see-through visor towards the surgical field.

A spinal surgery training process includes the steps of capturing a plurality of 2D images for each of a plurality of spines, generating a curve of each spine from the respective 2D images based on locations of select vertebrae in each of the spines, grouping the spines into one of a number of groups based on similarity to produce groups of spines having similarities, performing the capturing, generating, determining and grouping steps at least once prior to surgery and at least once after surgery to produce pre-operative groups and their resultant post-operative groups, and assigning surgical methods and a probability to each of the post-operative groups indicating the probability that a spinal shape of the post-operative group can be achieved using the surgical methods. An outcome prediction process for determining surgical methods can be implemented once the training process is complete.

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.

A system and method that provide the automatic extraction and processing of data from medical images from an image data archive. The processing including generating a metadata for an image, selecting an algorithm for image data analysis based on the metadata generated for the image, properties for at least two possible algorithms, and a specification of a specific image analysis to be performed, analyzing the image data with the algorithm selected from the at least two possible algorithms to produce results information, and linking the results information of image analysis and the metadata with referenceable anatomical structures of a human being or an animal within the image; and displaying the results linked to the anatomical structure.