Abstract: A system for customizing an implant is provided. The system includes a processor configured to: i) obtain one or more medical image stacks of a joint; ii) obtain a three-dimensional image representation of the joint based on at least one of said medical image stacks; iii) determine damage to the joint by analyzing said medical image stacks; iv) select an implant template from a predefined set of implant templates having predetermined types and sizes; v) generate a 3D model, in which the marked damage is visualized together with the selected implant template in a proposed position; vi) display the 3D model; vii) receive an approval for said selected implant template in said proposed position; and viii) determine the final shape and dimensions of a customized implant based on said selected implant template and said proposed position.

Abstract: The disclosure provides example embodiments for automatically or semi-automatically classifying cells in microscopic images of biological samples. These embodiments include methods for selecting training sets for the development of classifier models. The disclosed selection embodiments can allow for the re-training of classifier models using training examples that have been subjected to the same or similar incubation conditions as target samples. These selection embodiments can reduce the amount of human effort required to specify the training examples. The disclosed embodiments also include the classification of individual cells based on metrics determined for the cells using phase contrast imagery and defocused brightfield imagery. These metrics can include size, shape, texture, and intensity-based metrics. These metrics are determined based on segmentation of the underlying imagery.

Abstract: A first display control unit displays a first tomographic image of a three-dimensional image consisting of a plurality of tomographic images on a display unit, and superimposes and displays, as a first cross-section correction instruction region, a cross-section of a three-dimensional correction instruction region on the first tomographic image, which is used to correct a boundary of a region of interest extracted from the three-dimensional image, on the first tomographic image. A second display control unit displays at least one second tomographic image adjacent to the first tomographic image, which includes the three-dimensional correction instruction region, on the display unit, and superimposes and displays, as a second cross-section correction instruction region, a cross-section of the three-dimensional correction instruction region on the second tomographic image, on the second tomographic image.

Abstract: A method for a system and method for morphometric detection of malignancy associated change (MAC) is disclosed including the acts of obtaining a sample; imaging cells to produce 3D cell images for each cell; measuring a plurality of different structural biosignatures for each cell from its 3D cell image to produce feature data; analyzing the feature data by first using cancer case status as ground truth to supervise development of a classifier to test the degree to which the features discriminate between cells from normal or cancer patients; using the analyzed feature data to develop classifiers including, a first classifier to discriminate normal squamous cells from normal and cancer patients, a second classifier to discriminate normal macrophages from normal and cancer patients, and a third classifier to discriminate normal bronchial columnar cells from normal and cancer patients.

Abstract: A region determining device includes a likelihood calculating unit that calculates a likelihood of wetness of a pixel in an image including a plurality of frames, a non-water pixel measuring unit that measures the number of non-water pixels in which the likelihood falls within a first non-water range in any one of determination regions to be determined in the respective frames, a boundary pixel measuring unit that measures the number of boundary pixels in which the likelihood falls within a first water range in any one of the determination regions and the likelihood falls within a second non-water range in any one of the determination regions, and a region determining unit that determines whether the determination region satisfies a predetermined determination condition based on the number of non-water pixels and the number of boundary pixels.

Abstract: Provided is a system and computer-implemented method for encoding account tokens in image files. The method includes receiving, from a user associated with an account identifier, an identification of at least one image, generating at least one token based on the account identifier of the user, encoding the at least one token in the at least one image, resulting in at least one tokenized image, and communicating the at least one tokenized image to a transaction processing system, wherein the transaction processing system is configured to conduct a transaction based on the tokenized image.

Abstract: An ophthalmologic information processing apparatus according to the embodiments include a generator, an extractor, and a phase correction unit. The generator is configured to generate a phase difference profile by performing averaging processing in a depth direction on phase differences obtained by calculating for each depth position of A-lines between two adjacent B-frames of complex OCT data of a subject's eye. The extractor is configured to a phase drift from the phase difference profile generated by the generator. The phase correction unit is configured to correct a phase of first complex OCT data of any one of the two B-frames based on the phase drift extracted by the extractor.

Abstract: Systems and methods are disclosed for processing digital images to identify diagnostic tests, the method comprising receiving one or more digital images associated with a pathology specimen, determining a plurality of diagnostic tests, applying a machine learning system to the one or more digital images to identify any prerequisite conditions for each of the plurality of diagnostic tests to be applicable, the machine learning system having been trained by processing a plurality of training images, identifying, using the machine learning system, applicable diagnostic tests of the plurality of diagnostic tests based on the one or more digital images and the prerequisite conditions, and outputting the applicable diagnostic tests to a digital storage device and/or display.

Abstract: Embodiments of the present application provide a video control method, a video conference terminal and a multi-point control unit (MCU). The method comprises: performing human facial recognition on a video code stream and determining a motion quantity of the video code stream; sorting identifiers P and motion quantities R respectively corresponding to a plurality of video code streams; and selecting video code streams corresponding to video conference terminals ranked top M for synthesis and output and/or selecting a video conference terminal corresponding to a video code stream ranked first as a broadcast source.

Abstract: Provided are an image processing apparatus, a medical imaging apparatus, and an image processing program that remove noise from an image having different noise levels depending on regions in the image at a low calculation cost, and enable high quality according to a preference of a reader. A plurality of image generators receive measurement data or a captured image obtained by an image data acquisition apparatus and generate different images for a same imaging range. An image selection and combination unit selects different image regions from a plurality of images generated by the plurality of image generators according to a predetermined region selection pattern, and generates one image by combining the images of the selected image regions.

Abstract: Approaches for analyzing an input image and providing one or more outputs related to the input image are provided. In accordance with an exemplary embodiment, an input image may be received and analyzed, using a trained machine learning model, to generate an inference related to the image. Based, at least in part, upon the generated inference, one or more reports related to the inference can be generated and provided for presentation on a user device. A user can interact with the report in a conversational manner with the computer system to generate additional reports or insights related to the input image.

Abstract: A system and method configured to better identify patient-specific anatomical landmarks, measure anatomical parameters and features, and predict the patient's need for surgery within a predetermined time period. In some embodiments, the system and method is configured to predict the likelihood or risk that a patient will require total hip arthroplasty. In some embodiments, the present invention includes machine learning technology Some embodiments of the present invention include a first ML machine configured to received medical images as inputs and identify anatomical landmarks as outputs; a measurement module to measure joint space width, hip dysplasia angles, and/or leg length differential; and a second ML machine configured to receive the anatomical measurements and patient demographic data as inputs and produce a risk or likelihood that the patient will require surgery within a certain time frame.

Abstract: A system and a method for image-based crop identification are disclosed. The image-based crop identification system includes a database, a communication module and a model library. The database stores sample aerial data and annotated aerial data. The communication module is coupled to the database, and is configured to provide the sample aerial data to a user and receive the annotated aerial data from the user. The model library is coupled to the database, and is configured to obtain the annotated aerial data, train a crop classification model based on the annotated aerial data, and provide the trained crop classification model for subsequent crop identification. The annotated aerial data include determination of the type of the crop appearing in the sample aerial data.

Abstract: Provided is a control method for a system for determining a lesion obtained via real-time image. The control method comprises: an endoscope device obtaining a stomach endoscopy image; the endoscope device transmitting the obtained stomach endoscopy image to a server; the server determining a lesion included in the stomach endoscopy image, by inputting the stomach endoscopy image into a first artificial intelligence model; when it is determined that a lesion is detected in the stomach endoscopy image, the server obtaining an image including the lesion and transmitting the image to a database of the server; the server determining the type of the lesion included in the image, by inputting the image into a second artificial intelligence model; and when it is determined that a lesion is detected in the stomach endoscopy image, a display device displaying a UI for guiding the location of the lesion in the stomach endoscopy image.

Abstract: The present disclosure provides a method of embedding a watermark on a Joint Photographic Experts Group (JPEG) image. The method includes: performing an entropy decoding on the JPEG image to generate quantized discrete cosine transform (DCT) coefficients; determining target bits in a bit plane of the quantized DCT coefficients on the basis of a watermark-embedding table (WET); and embedding a watermark based on metadata of the JPEG image in the target bits. Also, the present disclosure provides a method of verifying integrity of the image by using the embedded watermark.

Abstract: A system is disclosed that includes an optical tracking device and a surgical computing device. The optical tracking device includes a structured light module and an optical module that includes an image sensor and is spaced from the structured light module at a known distance. The surgical computing device includes a display device, a non-transitory computer readable medium including instructions, and processor(s) configured to execute the instructions to generate a depth map from a first image captured by the image sensor during projection of a pattern into a surgical environment by the structured light module. The pattern is projected in a near-infrared (NIR) spectrum. The processor(s) are further configured to execute the stored instructions to reconstruct a 3D surface of anatomical structure(s) based on the generated depth map. Additionally, the processor(s) are configured to execute the stored instructions to output the reconstructed 3D surface to the display device.

Abstract: A method for judging freshness of cultured fish product based on eye image recognition is provided, which relates to the field of image processing and includes the following steps: obtaining eye area and eye center point of each cultured fish product; obtaining a first data category and a second data category of each gray scale change sequence according to each gray scale change sequence of each eye area; calculating a first mean value and a second mean value of each gray scale change sequence; obtaining the fish eye turbidity of each eye area according to the first mean value and the second mean value; obtaining the fish eye plumpness of each eye area according to the first data category and the second data category; and obtaining the freshness of each cultured fish product according to the fish eye turbidity and fish eye plumpness in each eye area.

Abstract: Systems and methods are disclosed for generating synthetic medical images, including images presenting rare conditions or morphologies for which sufficient data may be unavailable. In one aspect, style transfer methods may be used. For example, a target medical image, a segmentation mask identifying style(s) to be transferred to area(s) of the target, and source medical image(s) including the style(s) may be received. Using the mask, the target may be divided into tile(s) corresponding to the area(s) and input to a trained machine learning system. For each tile, gradients associated with a content and style of the tile may be output by the system. Pixel(s) of at least one tile of the target may be altered based on the gradients to maintain content of the target while transferring the style(s) of the source(s) to the target. The synthetic medical image may be generated from the target based on the altering.

Abstract: A background image generator (21) stores, in a storage device (3), a skeleton image obtained by calculating a feature quantity for each of multiple first thermal images (Din1) obtained by imaging by a thermal image sensor (1) in the same field of view or multiple sorted images (Dc) generated from the first thermal images, generating an average image from the first thermal images or sorted images, sharpening the average image, and then extracting a skeleton component. An image corrector (22) corrects, by using the skeleton image stored in the storage device (3), a second thermal image (Din2) obtained by imaging by the thermal image sensor in the same field of view as the first thermal images, thereby generating a corrected thermal image. It is possible to generate a sharp thermal image with a high S/N ratio.

Abstract: Techniques for training a first electronic neural network classifier to identify a presence of a particular property in a novel supra-image while ignoring a spurious correlation of the presence of the particular property with a presence of an extraneous property are presented.