Abstract: Systems and methods are disclosed for creating a mosaic image of two or more geo-referenced source images, the geo-referenced source images having the same orientation, based on a ground confidence map created by analyzing pixels of one or more of the geo-referenced source images, the ground confidence map having values and data indicative of particular geographic locations represented by the values, at least one of the values indicative of a statistical probability that the particular geographic locations represented by the values represents the ground; and using routes for steering mosaic cut lines based at least in part on the values indicative of the statistical probability that the particular geographic locations represented by the values represents the ground of the ground confidence map, such that the routes have an increased statistical probability of cutting through pixels representative of the ground versus routes not based on the ground confidence map.

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 system (100) includes a computer readable storage medium (122) with computer executable instructions (124), including: a biophysical simulator component (126) configured to determine a fractional flow reserve value via simulation and a traffic light engine (128) configured to track a user-interaction with the computing system at one or more points of the simulation to determine the fractional flow reserve value. A processor (120) is configured to execute the biophysical simulator component to determine the fractional flow reserve value and configured to execute the traffic light engine to track the user-interaction with respect to determining the fractional flow reserve value and provide a warning in response to determining there is a potential incorrect interaction. A display is configured to display the warning requesting verification to proceed with the simulation from the point, wherein the simulation is resumed only in response to the processor receiving the requested verification.

Abstract: The three-dimensional (3D) reconstruction of visible part of the human jaw is becoming required for many diagnostic and treatment procedures. The present invention improves upon Statistical Shape from Shading (SSFS) framework by using a novel approach to automatically extract prior information. This two-step framework consists of interdental gingiva regions extraction for each individual tooth and detection of the centerline across the jaw span. These two steps help extract the anatomical landmark points and detect the status of the jaw. Experimental results highlight the accuracy of the extracted prior information and how this information boosts recovering 3D models of the human jaw.

Abstract: The present disclosure provides user interface methods of and systems for displaying at least one available action overlaid on an image, comprising displaying an image; selecting at least one action and assigning a ranking weight thereto based on at least one of (1) image content, (2) current device location, (3) location at which the image was taken, (4) date of capturing the image; (5) time of capturing the image; and (6) a user preference signature representing prior actions chosen by a user and content preferences learned about the user; and ranking the at least one action based on its assigned ranking weight.

Abstract: A method includes training a machine learning model to generate predicted images to obtain a trained machine learning model, based on: a) pre-treatment training images; b) a plan of treatment; and c) post-treatment training images; where the plan of treatment includes: a) a first mark identifying where to apply a product, b) a first product to be applied at the first mark, and c) a first volume of the first product to be applied at the first mark; generating a predicted post-treatment image by applying the trained predictive visualization machine learning model to a new pre-treatment image, based on: a) a second mark on a new pre-treatment image of the area of a patient, b) a second product to be applied at the second mark, and c) a second volume of the second product to be applied at the second mark; where the predicted images identifies a modified area.

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: The present invention relates to deep learning implementations for medical imaging. More particularly, the present invention relates to a method and system for indicating whether additional medical tests are required after analysing an initial medical screening, in substantially real-time. Aspects and/or embodiments seek to provide a method and system for recommending additional medical tests, in substantially real-time, based on analysing an initial medical scan, with the use of deep learning.

Abstract: An imaging system comprises multiple light sources, a beam combiner, an optical array sensor, and a computing device. A first light source forms a first beam of light at a first wavelength. A second light source forms a second beam of light at a second wavelength. The beam combiner combines the first beam of light and the second beam of light into a single beam of light and illuminates a specimen with the single beam of light. The optical array sensor detects reflected light that is reflected from the specimen. The computing device accesses sensor data from the optical array sensor, forms a first image based on the first wavelength and a second image based on the second wavelength, and forms a composite image from the first image and the second image.

Abstract: The present invention relates to deep learning implementations for medical imaging. More particularly, the present invention relates to a method and system for suggesting whether to obtain a second review after a first user has performed a manual review/analysis of a set of medical images from an initial medical screening. Aspects and/or embodiments seek to provide a method and system for suggesting that a second radiologist reviews one or more cases/sets of medical images in response to a first radiologist's review of the case of medical images, based on the use of computer-aided analysis (for example using deep learning) on each case/set of medical images and the first radiologist's review.

Abstract: A medical image processing apparatus 10 includes a medical image acquisition unit 11 that acquires a medical image 50 including a subject image, a medical image analysis result acquisition unit 12 that acquires an analysis result obtained by analyzing the medical image 50, a display unit 13 that displays at least one medical image 50 and at least information on presence or absence of a lesion or a type of a lesion in the analysis result acquired by the medical image analysis result acquisition unit 12, and an input receiving unit 14 that receives an input regarding whether or not the information on presence or absence of a lesion or a type of a lesion included in the analysis result is correct.

Abstract: In part, the invention relates to systems and methods of calibrating a plurality of frames generated with respect to a blood vessel as a result of a pullback of an intravascular imaging probe being pullback through the vessel. A calibration feature disposed in the frames that changes between a subset of the frames can be used to perform calibration. Calibration can be performed post-pullback. Various filters and image processing techniques can be used to identify one or more feature in the frames including, without limitation, a calibration feature, a guidewire, a side branch, a stent strut, a lumen of the blood vessel, and other features. The feature can be displayed using a graphic user interface.

Abstract: Described are systems and methods for training a machine-learning model to generate image of biological samples, and systems and methods for generating enhanced images of biological samples. The method for training a machine-learning model to generate images of biological samples may include obtaining a plurality of training images comprising a training image of a first type, and a training image of a second type. The method may also include generating, based on the training image of the first type, a plurality of wavelet coefficients using the machine-learning model; generating, based on the plurality of wavelet coefficients, a synthetic image of the second type; comparing the synthetic image of the second type with the training image of the second type; and updating the machine-learning model based on the comparison.

Abstract: Computer-implemented techniques for classifying a tissue specimen are presented. The techniques include obtaining an image of the tissue specimen; segmenting the image into a first plurality of segments; selecting a second plurality of segments that include at least one region of interest; applying an electronic convolutional neural network trained by a training corpus including a set of pluralities of tissue sample image segments, each of the pluralities of tissue sample image segments labeled according to one of a plurality of primary pathology classes, where the plurality of primary pathology classes consist of a plurality of majority primary pathology classes, where the plurality of majority primary pathology classes collectively include a majority of pathologies according to prevalence, and a class for tissue sample image segments not in the plurality of majority primary pathology classes, such that a primary pathology classification is output; and providing the primary pathology classification.

Abstract: Methods and apparatus for evaluating an impact of injury to brain networks or regions are provided. The method comprises receiving MRI data of a brain of an individual, including a first volumetric dataset recorded using first imaging parameters and a second volumetric dataset recorded using second imaging parameters, combining, on a voxel-by-voxel basis, first MRI data based on the first volumetric dataset and second MRI data based on the second volumetric dataset to produce a volumetric injury map, performing a structural-functional analysis of one or more brain networks or regions by refining the volumetric injury map using a volumetric eloquence map that specifies eloquent brain tissue within the one or more brain networks or regions to determine an impact of injury within the one or more brain networks or regions, and displaying a visualization of the determined impact of injury within the one or more brain networks or regions.

Abstract: Aspects of the disclosure provide methods and apparatuses for point cloud compression and decompression. In some examples, an apparatus for point cloud compression/decompression includes processing circuitry. For example, the processing circuitry in the apparatus for point cloud encoding receives an occupancy map for a point cloud. The occupancy map is indicative of a background portion and a foreground portion for a coding block in an image that is generated based on the point cloud. Then, the processing circuitry devaluates distortions in the background portion of the coding block during an optimization process that results a coding option for the coding block, and encodes the coding block according to the coding option.

Abstract: A method, system and computer program product for segmenting generic foreground objects in images and videos. For segmenting generic foreground objects in videos, an appearance stream of an image in a video frame is processed using a first deep neural network. Furthermore, a motion stream of an optical flow image in the video frame is processed using a second deep neural network. The appearance and motion streams are then joined to combine complementary appearance and motion information to perform segmentation of generic objects in the video frame. Generic foreground objects are segmented in images by training a convolutional deep neural network to estimate a likelihood that a pixel in an image belongs to a foreground object. After receiving the image, the likelihood that the pixel in the image is part of the foreground object as opposed to background is then determined using the trained convolutional deep neural network.

Abstract: A method and a system for planning an orthodontic treatment are provided. The method comprises: acquiring an arch form 3D digital model including a representation of the given tooth in a current position thereof within a subject's gingiva; determining an initial crown reference point and an initial root reference point; obtaining a target position of the given tooth within the arch form 3D digital model; determining a number of steps for the given tooth to displace from the current position to the target position thereof in the course of the orthodontic treatment; and storing data indicative of the number of steps associated with the given tooth for use in the planning the orthodontic treatment.

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: 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.