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.

The present disclosure provides methods, systems, and non-transitory computer-readable media for assessment of disease treatment or progression on a lesion-by-lesion level. The systems and methods are based on measurements of a variety of features including total number of lesions, total number and proportion of lesions regressing or progressing, changes in dimensions of a lesion over time, and uptake values of a molecular imaging agent.

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 locally enhancing medical images. In accordance with certain embodiments, a method includes determining a boundary of a region of interest in a displayed medical image, overlaying the boundary on the displayed medical image, adjusting a position of a collimator of a medical imaging system based on the determined boundary, enhancing image quality of the region of interest, and displaying the enhanced region of interest within the boundary.

Systems and methods for inspecting the outer skin of a honeycomb body are provided. The inspection system comprises a rotational sub-assembly configured to rotate the honeycomb body, a camera sub-assembly configured to image at least a portion of the outer skin of the honeycomb body as it rotates, a three-dimensional (3D) line sensor sub-assembly configured to obtain height information from the outer skin of the honeycomb body; and an edge sensor sub-assembly configured to obtain edge data from the circumferential edges of the honeycomb body. In some examples, the inspection system utilizes a universal coordinate system to synchronize or align the data obtain from each of these sources to prevent redundant or duplicative detection of one or more defects on the outer skin of the honeycomb body.

A system includes a processor and a non-transitory computer-readable medium containing instructions that when executed by the processor causes the processor to perform operations comprising displaying a prompt to a user of a mobile device on a display of a mobile device to capture an image representing at least a portion of a mouth of the user using a rear-facing camera of the mobile device, where the rear-facing camera is on an opposite side of the mobile device including the display. The operations further comprise controlling the rear-facing camera to enable the rear-facing camera to capture the image, receiving the image, and outputting, user feedback based on the image, where the user feedback is outputted on the display that is on the opposite side of the mobile device than the rear-facing camera.

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.

A part inspection system includes a vision device configured to image a part being inspected and generate a digital image of the part. The system includes a part inspection module communicatively coupled to the vision device and receives the digital image of the part as an input image. The part inspection module includes a defect detection model. The defect detection model includes a template image. The defect detection model compares the input image to the template image to identify defects. The defect detection model generates an output image. The defect detection model configured to overlay defect identifiers on the output image at the identified defect locations, if any.

A method for parallel processing a digitally scanned pathology image is performed by a plurality of processors and includes performing, by a first processor, a first operation of generating a first batch from a first set of patches extracted from a digitally scanned pathology image and providing the generated first batch to a second processor, performing, by the first processor, a second operation of generating a second batch from a second set of patches extracted from the digitally scanned pathology image and providing the generated second batch to the second processor, and performing, by the second processor, a third operation of outputting a first analysis result from the first batch by using a machine learning model, with at least part of time frame for the second operation performed by the first processor overlapping at least part of time frame for the third operation performed by the second processor.

An image processing device includes a hardware processor. The hardware processor designates, from one frame image of a dynamic image acquired by dynamic imaging of a movement of a locomotorium, a plurality of regions or points on a structure included in the locomotorium, sets an alignment reference based on the designated regions or points, tracks the designated regions or points in a plurality of frame images of the dynamic image, aligns a line segment connecting the regions or the points to each other in the plurality of frame images based on the alignment reference, and causes a display to display the line segment so as to be superimposed on a representative frame image of the dynamic image.