A method for acquiring at least one two-dimensional image of a part of arches of a patient includes steps carried out by the patient or other person who is not a dental health professional, for example, including placing a dental separator in the mouth of the patient in order to separate the lips of the patient and improve the visibility of the teeth during the acquisition of said at least one two-dimensional image, and acquiring, in a mouth closed position and with a personal image acquisition apparatus, said at least one two-dimensional image.

A computer device obtains a medical image set. The device identifies a difference between the reference medical image and the target medical image to obtain a candidate non-lesion region in the target medical image. The device determines area size information of the candidate non-lesion region as candidate area size information. The device adjusts the candidate non-lesion region according to the annotated area size information when the candidate area size information does not match the annotated area size information, so as to obtain a target non-lesion region in the target medical image.

A method of analyzing thoracic insufficiency syndrome (TIS) in a subject by performing quantitative dynamic magnetic resonance imaging (QdMRI) analysis. The QdMRI analysis includes performing four-dimensional (4D) image construction of a TIS subject's thoracic cavity. The 4D image includes a sequence of two dimensional (2D) images of the TIS subject's thoracic cavity over a respiratory cycle of the TIS subject. The QdMRI analysis also includes segmenting a region of interest (ROI) within the 4D image, determining TIS measurements within the ROI, comparing the TIS measurements to normal measurements determined from ROIs in 4D images of the thoracic cavities of normal subjects that are not afflicted by TIS, and outputting quantitative markers indicating deviation of the thoracic cavity of the TIS subject relative to the thoracic cavities of the normal subjects.

Disclosed are a method and device for analyzing human tissue on the basis of a medical image. A tissue analysis device generates training data including a two-dimensional medical image and volume information of tissue by using a three-dimensional medical image, and trains, by using the training data, an artificial intelligence model that obtains a three-dimensional size, volume, or weight of tissue by dividing at least one or more normal or diseased tissues from a two-dimensional medical image in which a plurality of tissues are displayed overlapping on the same plane. In addition, the tissue analysis device obtains a three-dimensional size, volume, or weight of normal or diseased tissue from an X-ray medical image by using the artificial intelligence model.

A geographic position of an agricultural machine is captured. Agricultural data is received that corresponds to a geographic position. Georeferenced visual indicia are displayed that are indicative of the received agricultural data.

Disclosed embodiments determine, at an early stage, suitability of an intraoperative image for further intraoperative surgical analysis. The determination of suitability may be made using a first angle (such as a first obturator angle) based on at least three pelvic feature points in a preoperative image, a corresponding second angle (such as a corresponding second obturator angle) based on at least three corresponding pelvic feature points in an intraoperative image, and by comparing the first angle and the corresponding second angle to determine intraoperative image suitability. The first intra-operative image is indicated as suitable for further intraoperative analysis when an absolute value of a difference between the first angle and the corresponding second angle does not exceed a threshold. When the intraoperative image is determined as unsuitable for further intraoperative analysis, an indication of a movement direction for a fluoroscopy camera used to capture the intraoperative image is provided.

A registration chaining system provides information transfer along a chain of registrations of images of same or different modalities. A registration at each link is based on a shared feature readily distinguished in a pair of images. The information is transferred using the registration.

Accurate tissue segmentation is performed without a priori knowledge of tissue type or other extrinsic information not found within the subject image, and may be combined with classification analysis so that diseased tissue is not only delineated within an image but also characterized in terms of disease type. In various embodiments, a source image is decomposed into smaller overlapping subimages such as square or rectangular tiles. A predictor such as a convolutional neural network produces tile-level classifications that are aggregated to produce a tissue segmentation and, in some embodiments, to classify the source image or a subregion thereof.

The present disclosure relates to a method and an apparatus for measuring motility of ciliated cells in a respiratory tract. The method includes the operations of: acquiring image data including a plurality of frames of respiratory tract organoids; identifying positions of ciliated cells by performing motion-contrast imaging on the image data; when a region of interest (ROI) related to the position of the ciliated cells is selected, measuring a ciliary beat frequency (CBF) related to motility of cilia included in the selected region of interest using cross-correlation between the plurality of frames; and expressing the cilia included in the region of interest in a preset display method on the basis of the range of the measured ciliary beat frequency.

Disclosed is a gamification system for overlaying user-controlled graphical targeting elements over a real-time video feed of an instrument being inspected, and providing interactive controls for firing virtual weapons or other graphical indicators to designate and/or record the presence of contaminants, defects, and/or other issues at specific locations within or on the instrument. The system may receive and present images of the instrument under inspection in a graphical user interface (“GUI”). The system may receive user input that tags a particular region of a particular image with an issue identifier, and may generate a visualization that is presented in conjunction with the particular image in the GUI in response to receiving the input. The visualization corresponds to firing of a virtual weapon and other gaming visuals associated with tagging the particular region of the particular image with the issue identifier.