Example systems and methods correctly align or register a first image with a second image using user input to identify location(s) of interest in the overlay image. The system can ask a user to select a vertebral level of interest on a screen displaying the baseline and/or the overlay image. Then, the user input can be advantageously to guide subsequent image registration steps. The systems and methods herein may also be used to augment an existing image-recognition algorithm.

A method is provided for measuring using images. The method includes displaying an image having varying intensities. The method also includes identifying an analysis line passing through at least one feature of the image. Further, the method includes displaying an intensity graph over the image. The intensity graph includes a first axis extending along the analysis line and a second axis corresponding to intensities of points of the image along the analysis line. The method also includes identifying points of interest along the analysis line based on the intensity graph. Also, the method includes measuring at least one distance between at least two of the points of interest.

A procedure is performed with respect to a skeletal portion within a body with a tool mounted upon a steerable arm. 3D image data is acquired of the skeletal portion. First and second x-ray images of the tool and the skeletal portion are acquired from respective first and second views. A computer processor (22) (i) registers the first and second images to the image data, (ii) identifies a location of the tool with respect to the skeletal portion, within the first and second images, (iii) determines the tool's location with respect to the image data, (iv) compares the tool's location with a designated locational element, (v) calculates the 3D difference between the tool's location and the locational element, and (vi) generates steering instructions for the arm such that the tool's location matches the locational element. Other applications are also described.

According to some embodiments, the process includes the steps of: a) taking a sagittal preoperative x-ray of the vertebral column of the patient to be treated, extending from the cervical vertebrae to the femoral heads; b) on that x-ray, identifying points on S1, S2, T12 et C7; c) depicting, on the said x-ray, curved segments beginning at the center of the plate of Si et going to the center of the plate of C7; e) identifying, on that x-ray, the correction(s) to be made to the vertebral column, including the identification of posterior osteotomies to make; f) pivoting portions of said x-ray relative to other portions of that x-ray, according to osteotomies to be made; g) performing, on said x-ray, a displacement of the sagittal curvature segment extending over the vertebral segment to be corrected; h) from a straight vertebral rod (TV), producing the curvature of that rod according to the shape of said sagittal curvature segment in said displacement position.

Methods and systems for performing computer-assisted image-guided surgery, including robotically-assisted surgery. A method of displaying image data includes displaying image data of a patient on a handheld display device, tracking the handheld display device using a motion tracking system, and modifying the image data displayed in response to changes in the position and orientation of the handheld display device. Further embodiments include a sterile case for a handheld display device, display devices on a robotic arm, and methods and systems for performing image-guided surgery using multiple reference marker devices fixed to a patient.

The present invention discloses a method and a system of vertebral compression fracture detection. The method of vertebral compression fracture detection includes: recombining a plurality of anatomical images captured in at least a spine segment of a target individual into a 3D image; using a multi-planar reconstruction method to reformat the 3D image to obtain at least one sagittal reformatted image; using a classification model to determine whether the sagittal reformatted image covers the middle section of the vertebral column or not; using a vertebral detection method to detect each vertebral body in the sagittal reformatted image covering the middle section of the vertebral column; using a keypoint localization method to localize a plurality of keypoints of each vertebral body which was detected in the sagittal reformatted image; evaluating the compression fracture grade of each vertebral body in the sagittal reformatted image.

Methods and systems for detecting a vertebral fracture within an x-ray. One method includes receiving a chest x-ray image and identifying a plurality of vertebrae represented in the chest x-ray image. The method further includes extracting a plurality of image patches from the chest x-ray image, each image patch of the plurality of image patches including a portion of the chest x-ray image representing one of the plurality of vertebrae identified in the chest x-ray image. The method further includes sequencing the plurality of image patches into an ordered sequence of image patches, and assigning, with a deep learning model applied to the ordered sequence of image patches, a classification to each of the plurality of image patches indicating whether the image patch represents a fractured vertebra or an unfractured vertebra.

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 method for identifying a body region in a medical image includes obtaining a medical image including a number of consecutive bio-section images, inputting the medical image into a preset machine learning model to obtain a numerical value for each of the bio-section images corresponding to the body region to which the bio-section image belongs, determining whether the numerical values of the medical image are abnormal, adjusting the numerical values when the numerical values are abnormal, determining the body region corresponding to the numerical values or the adjusted numerical values, and labeling the body region in the medical image and outputting the labeled medical image. The bio-section images are cross-sectional images of a living body.

Image data including a target object is acquired. The target object includes at least one sub-object. Target image data is acquired by processing the image data based on a fully convolutional neural network. The target image data include at least a center point of each sub-object in the target object.