The subject disclosure provides systems and methods for determination of Area of Interest (AOI) for different types of input slides. Slide thumbnails may be assigned into one of five different types, and separate algorithms for AOI detection executed depending on the slide type. Slide types include ThinPrep (RTM) slides, tissue micro-array (TMA) slides, control HER2 slides with 4 cores, smear slides, and a generic slide. The slide type may be assigned based on a user input. Customized AOI detection operations are provided for each slide type. If the user enters an incorrect slide type, operations include detecting the incorrect input and executing the appropriate method. The result of each AOI detection operations provides as its output a soft-weighted image having zero intensity values at pixels that are detected as not belonging to tissue, and higher intensity values assigned to pixels detected as likely belonging to tissue regions.

An image segmentation method is disclosed that allows a user to select image component types, for example tissue types and or background, and have the method of the present invention segment the image according to the user's input utilizing the superpixel image feature data and spatial relationships.

A system, device, application stored on non-transitory memory, and method can be configured to help a user of a device locate and pick up objects around them. Embodiments can be configured to help vision-impaired users find, locate, and pickup objects near them. Embodiments can be configured so that such functionality is provided locally via a single device so the device is able to provide assistance and hand guidance without a connection to the internet, a network, or another device (e.g. a remote server, a cloud based server, a server connectable to the device via an application programming interface, API, etc.).

The present disclosure relates to a method, performed by at least one processor of an information processing system, of analyzing a pathological image. The method includes receiving a pathological image, detecting an object associated with medical information, in the received pathological image by using a machine learning model, generating an analysis result on the received pathological image, based on a result of the detecting, and outputting medical information about at least one region included in the pathological image, based on the analysis result.

Provided is a classification condition setting support apparatus including: a basic information storage unit configured to store basic information including basic imaging information and a basic defect type; a classification condition setting unit; a basic defect type classification unit configured to classify the basic imaging information according to the classification condition; a classification result confirmation screen generator configured to generate a classification result confirmation screen including the number of pieces of classification basic imaging information, the basic defect type associated with the classification basic imaging information, and the number of pieces of correct answer basic imaging information, by classifying the target basic imaging information according to the classification condition; and a display unit.

A method and system for facilitating identification and marking of a target in a displayed Fluoroscopic Three-Dimensional Reconstruction (F3DR) of a body region of a patient. The system includes a display and a storage device storing instructions for receiving an initial selection of the target in the F3DR, fining the F3DR based on the initial selection of the target, displaying the fined F3DR on the display, and receiving a final selection of the target in the fined F3DR via a user selection. The system further includes at least one hardware processor configured to execute said instructions. The method and instructions may also include receiving a selection of a medical device in two two-dimensional fluoroscopic images, where the medical device is located in an area of the target, and initially fining the F3DR based on the selection of the medical device.

An electronic apparatus according to the present invention, includes at least one memory and at least one processor which function as: an acquisition unit configured to acquire positional information indicating a position of an object in a captured image; a display control unit configured to perform control such that an item having a length in a first direction, which corresponds to a range in a depth direction in the image, is displayed in a display, and a graphic indicating presence of the object is displayed in association with a position corresponding to the positional information in the item; a reception unit configured to be able to receive an operation of specifying a set range which is at least part of the item; and a processing unit configured to perform predetermined processing based on the set range.

A vision inspection system includes a vision inspection controller receiving images form an imaging device. The vision inspection controller includes a binary classification tool and a multi-classification tool. The vision inspection controller processes each of the images through the binary classification tool to detect for the defects to determine primary inspection results including a PASS result if no defects are detected and a FAIL result if defects are detected. The vision inspection controller processes each of the images associated with the FAIL result through the multi-classification tool to determine secondary inspection results including identification of a type of defect. The vision inspection system may include a display configured to display the primary and secondary inspection results to an operator.

A computing entity is described that obtains at least one inspection image of an at least partially fabricated product and causes the at least one inspection image to be processed by a product inspection engine. The product inspection engine includes a machine learning-trained model. The computing entity obtains an inspection result determined based on the processing of the at least one inspection image by the product inspection engine; identifies one or more training images stored in an image database based at least in part on the at least one inspection image; associates automatically generated labeling data with the one or more training images based at least in part on the inspection result determined by the processing of the at least one inspection image; and causes training of the product inspection engine using the one or more training images and the associated labeling data.

An image processing device is an image processing device configured to cause a display to display, as a three-dimensional image, three-dimensional data representing a biological tissue having a longitudinal lumen. The image processing device includes: a control unit configured to calculate centroid positions of a plurality of cross sections in a lateral direction of the lumen of the biological tissue by using the three-dimensional data, set a pair of planes intersecting at a single line passing through the calculated centroid positions as cutting planes, and form, in the three-dimensional data, an opening exposing the lumen of the biological tissue from a region interposed between the cutting planes in the three-dimensional image.