A system or method according to at least one embodiment of the present disclosure includes receiving a preoperative image of a patient in a first posture; receiving an intraoperative image of the patient in a second posture; comparing the preoperative image of the patient in the first posture with the intraoperative image of the patient in the second posture; and determining, based on comparing the preoperative image of the patient in the first posture with the intraoperative image of the patient in the second posture, a difference between the first posture and the second posture, the adequate surgical changes are imparted to the second posture to achieve satisfactory surgical outcome.

Techniques are described for learning feature representations of medical images using a self-supervised learning paradigm and employing those feature representations for automating downstream tasks such as image retrieval, image classification and other medical image processing tasks. According to an embodiment, computer-implemented method comprises generating alternate view images for respective medical images included in set of training images using one or more image augmentation techniques or one or more image selection techniques tailored based on domain knowledge associated with the respective medical images. The method further comprises training a transformer network to learn reference feature representations for the respective medical images using their alternate view images and a self-supervised training process. The method further comprises storing the reference feature representations in an indexed data structure with information identifying the respective medical images that correspond to the reference feature representations.

A surgical navigation system includes a source of a patient anatomy data, wherein the patient anatomy data comprises a three-dimensional reconstruction of a segmented model comprising at least two sections representing parts of the anatomy. A surgical navigation image generator is configured to generate a surgical navigation image comprising the patient anatomy. A 3D display system is configured to show the surgical navigation image wherein the display of the patient anatomy is selectively configurable such that at least one section of the anatomy is displayed and at least one other section of the anatomy is not displayed.

The present disclosure provides a system and method for image data acquisition. The method may include obtaining image data of a subject including a first type of tissue and a second type of tissue. The method may include determining, based on the image data of the subject, a target portion including at least a portion of at least one of the first type of tissue or the second type of tissue. The method may include determining, based at least in part on the target portion represented in the image data, a scan mode corresponding to the target portion. The method may include causing an imaging device to acquire, based on the scan mode, image data of the target portion.

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.

The present disclosure relates to a method and a data processing system for generating navigable images of at least one region of interest ROI of a patient for a fluoroscopy-based navigation system using an X-ray imaging system, a localization system and an imaging kit, said imaging kit being configured to allow images registration in a preferred referential and tracking of surgical tools.

The present application provides a method and system for acquiring centerline of aorta based on CT sequence images. The method comprises: acquiring three-dimensional data of CT sequence images; acquiring a gravity center of heart and a gravity center of spine based on the three-dimensional data; filtering impurity data from the three-dimensional data of CT sequence images to obtain an image containing left atrium, left ventricle and without interfering coronary artery tree; layered slicing to obtain a group of binarized images; obtaining a circle center and an radius from each layer of slice in the group of binarized images, to generate a point list and an radius list; and mapping one or more pixel points in the point list and the radius list to the image to obtain a centerline of aorta.

Segmentation of multi-energy CT data, including data in three or more energy bands. A user is enabled to input one or more region indicators in displayed CT data. Probability maps are generated and may be refined using distance metrics, which may include geodesic and Euclidean distance metrics. Segmentation may be based on the probability maps and/or refined probability maps. Segmentation of medical image data is also disclosed.

A method for registering a two-dimensional image data set with a three-dimensional image data set of a body of interest is discloses herein. The method includes the following steps: obtaining a first spatial parameter of a first registered virtual camera, wherein the first registered virtual camera is positioned corresponding to a first two-dimensional image of the two-dimensional image data set; and adjusting a second spatial parameter of the first unregistered virtual camera with the first spatial parameter of the first registered virtual camera, wherein the first unregistered virtual camera is failed to be positioned corresponding to the first two-dimensional image of the two-dimensional image data set.

In an example method, a computer system receives one or more images of one or more bones of a patient. The one or more images are generated by a magnetic resonance imaging (MRI). The computer system determines one or more metrics indicative of an image texture of the one or more images; and determines at least one of a bone risk or a bone health of the patient based on the one or more metrics.