A medical system for use in a lithotripsy procedure may include a processor configured to receive input from a first imaging device, wherein the first imaging device may be configured to send image data representative of an image captured in a lumen of a kidney, bladder, or ureter to the processor. The processor may be configured to display the image on a display device coupled to the processor, and analyze the image to sense the presence of an object within the image. If an object was sensed within the image, the processor may analyze the image to estimate a size of the object, and display the estimate on the display device.

A facility identifies anatomical objects visualized by a medical imaging image. The facility applies two machine learning models to the image: a first trained to predict a view probability vector that, for each of a list of views, attributes a probability that the image was captured from the view, and a second trained to predict an object probability vector that, for each of a list of anatomical objects, attributes a probability that the object is visualized by the image. For each object, the facility: (1) accesses a list of views in which the object is permitted; (2) multiplies the predicted probability that the object is visualized by the image by the sum of the predicted probabilities that the accessed image was captured from views in which the object is permitted; and (3) where the resulting probability exceeds a threshold, determines that the object is visualized by the accessed image.

A medical system for use in a lithotripsy procedure may include a processor configured to receive input from a first imaging device, wherein the first imaging device may be configured to send image data representative of an image captured in a lumen of a kidney, bladder, or ureter to the processor. The processor may be configured to display the image on a display device coupled to the processor, and analyze the image to sense the presence of an object within the image. If an object was sensed within the image, the processor may analyze the image to estimate a size of the object, and display the estimate on the display device.

A method and a product for multi-device AI linkage displaying of a VRDS 4D medical image, the method includes: extracting first partial image data from a target 4D image data according to preset raw spatial attitude information and display screen parameters of a first display device, and extracting second partial image data from the target 4D image data according to the raw spatial attitude information and display screen parameters of a second display device; and displaying the first partial image data on the first display device and displaying the second partial image data on the second display device; judging whether to enable a linkage display function when receiving first spatial attitude information and second spatial attitude information; if so, selecting the spatial attitude information of a display device with high display priority as reference spatial attitude information; adjusting image data displayed by a display device with low display priority.

A method for image segmentation comprises receiving volumetric image data for an anatomical region and generating a first volumetric patch from the volumetric image data. The method also comprises generating a second volumetric patch from the first volumetric patch by weighting a plurality of volumetric units in the first volumetric patch and receiving the second volumetric patch as an input to a convolutional neural network. The method also comprises conducting a down-sampling filter process and conducting an up-sampling filter process within the convolutional neural network.

An information processing apparatus comprising at least one processor, wherein the at least one processor is configured to: acquire a document describing a subject; extract document finding information indicating a finding of the subject included in the document; and specify a finding extraction process for extracting image finding information indicating the finding indicated by the document finding information from a first image obtained by imaging the subject, among a plurality of types of finding extraction processes for extracting image finding information indicating a plurality of different types of findings that are able to be included in the first image.

An ultrasound imaging apparatus includes: an image processor configured to generate an ultrasound image based on an ultrasound echo signal; a display; and a main controller configured to detect a liver area and a kidney area in the ultrasound image, extract a border line between the liver area and the kidney area, automatically establish a region of interest of the liver and a region of interest of the kidney based on the border line, obtain a diagnostic parameter for the region of interest of the liver and a diagnostic parameter for the region of interest of the kidney, and control the display to display information about the diagnostic parameter for the region of interest of the liver and the diagnostic parameter for the region of interest of the kidney.

Systems, devices, and methods for endoscopic mapping of a target and tracking of endoscope locations inside a subject's body during a procedure are disclosed. An exemplary system comprises an imaging system configured to capture an endoscopic image of the target that includes a footprint of an aiming beam directed at the target, and a video processor configured to identifying one or more landmarks from the captured endoscopic image and determine their respective locations relative to the aiming beam footprint, and generate a target map by integrating a plurality of endoscopic images based on landmarks identified from one or more of the plurality of endoscopic images. The target map can be used to track endoscope location during an endoscopic procedure.

A method, software and apparatus are disclosed to improve security by helping determine whether a bag contains a prohibited item. A CT scan of a bag is performed to generate an image. An artificial intelligence algorithm is performed to classify portions of an image as normal and portions of said image as abnormal. A first type of image processing for said normal portion(s) of said image. A second type of image processing for said abnormal portion(s) of said image wherein said second type of image processing is different from said first type of image processing. The normal portion(s) of the image are displayed with said first type of image processing and said abnormal portion(s) of the image are displayed with said second type of image processing to a TSA Agent for analysis of said image. In the preferred embodiment, the TSA Agent will perform the analysis on an extended reality head display unit.

Systems and methods for managing motions of an anatomical region of interest of a patient during image-guided radiotherapy are disclosed. An exemplary system may include an image acquisition device, a radiotherapy device, and a processor device. The processor device may be configured to control the image acquisition device to acquire at least one 2D image. Each 2D image may include a cross-sectional image of the anatomical region of interest. The processor device may also be configured to perform automatic contouring in each 2D image to extract a set of contour elements segmenting the cross-sectional image of the anatomical region of interest in that 2D image. The processor device may be further configured to match the set of contour elements to a 3D surface image of the anatomical region of interest to determine a motion of the anatomical region of interest and to control radiation delivery based on the determined motion.