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.

An image analyzation method and an image analyzation device are disclosed. The method includes: obtaining a first image which presents at least a first object and a second object; analyzing the first image to detect a first central point between a first endpoint of the first object and a second endpoint of the second object; determining a target region based on the first central point as a center of the target region; capturing a second image located in the target region from the first image; and analyzing the second image to generate status information which reflects a gap status between the first object and the second object.

A method and imaging system for contouring medical images is described. The method comprising: receiving at least one input 2D image slice, from a set of two-dimensional (2D) image slices constituting the 3D image, and at least one set of data representing an input contour identifying one or more structures of interest in the 3D image within the at least one input 2D image slice; receiving at least one selected target image slice, from the set of the 2D image slices; and predicting target contour data for the selected target image slice that identifies at least one of the same one or more structures of interest within the target image slice, based on one or more of the received input 2D image slices and the data representing an input contours.

A method for determining an acceptable spinal surgical plan for a subject using pathology prediction, comprising generating a potential spinal surgical plan, obtaining clinically relevant data of the subject, obtaining pre-operative three-dimensional images of a spinal region of the subject, determining relationships between pairs of vertebrae in the images, predicting relationships between pairs of vertebrae that are expected from the surgical plan, accessing a multiple patient database, obtaining sets of data from the database for patients with similar characteristics to the subject, determining risks of pathology types for the subject, using artificial intelligence to combine the determined risks to calculate an overall risk for pathology types for the subject, and if the overall risks are unacceptable, selecting an alternative spinal surgical plan, and if the said overall risks are acceptable, determining that said surgical plan is acceptable.

A method of detecting a vertebral endplate rim according to embodiments of the present disclosure includes receiving, at a processor, a binary mask of a vertebra; applying a Laplacian filter to the binary mask to yield a processed image; identifying, in the processed image, a set of edges having a sharpness that exceeds a predetermined threshold; deformably registering a template to a plurality of points corresponding to at least some of the edges in the set of edges; excluding some of the plurality of points based on at least one predetermined criterion; and generating additional points by interpolating among a remaining subset of the plurality of points to yield a set of points defining an endplate rim of the vertebra.

A method comprising segmenting at least one vertebral body from at least one image of a first three-dimensional image data set. The method comprises receiving at least one image of a second three-dimensional image data set. The method comprises registering the segmented at least one vertebral body from the at least one image of the first three-dimensional image data set with the at least one image of the second three-dimensional image data set. The method comprises determining a position of the at least one surgical implant based on the at least one image of the second three-dimensional image data set and a three-dimensional geometric model of the at least one surgical implant. The method comprises overlaying a virtual representation of the at least one surgical implant on the registered and segmented at least one vertebral body from the at least one image of the first three-dimensional image data set.

Ultrasound imaging methods for identifying an anatomical feature of a spine are described. In an embodiment, the method comprises: receiving a transverse ultrasound image of a portion of the spine; extracting features of the portion of the spine from the image based on a distinct pattern associated with the anatomical feature of the spine; identifying a midline of the portion of the spine in the image; extracting midline features using pixel intensity values of the image; and identifying, based on a combination of the extracted features of the portion of the spine and the extracted midline features, the anatomical feature in the image. In another embodiment, the method comprises: receiving a paramedian sagittal ultrasound image of a portion of the spine; identifying morphological features of the image; and determining if the portion of the spine includes a sacrum using a Support Vector Machine classifier.

Certain systems, methods, and devices described herein are configured to dynamically model a patient area for surgery and/or other treatment, dynamically identify one or more features and/or characteristics thereon such as the length and/or elasticity of the posterior longitudinal ligament (PLL), dynamically allow modification of the model, dynamically limit and/or assist in modification of the model, and/or dynamically generate guidelines for generation of patient-specific implants and/or treatment kits for a specific patient.

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.

A system may be configured to facilitate a medical procedure. Some embodiments may: acquire a scan corresponding to a region of interest (ROI); capture an image of a patient in real-time; identify, via a trained machine learning (ML) model using the acquired scan and the captured image, a Kambin's triangle; and overlay, on the captured image, a representation of the identified Kambin's triangle.