Abstract: Risk prediction models are trained and deployed to analyze images, such as computed tomography scans, for predicting future risk of lung cancer for one or more subjects. Individual risk prediction models are separately trained on nodule-specific and non-nodule specific features such that each risk prediction model can predict future risk of lung cancer across different time periods (e.g., 1 year, 3 years, or 5 years). Such risk prediction models are useful for developing preventive therapies for lung cancer by enabling clinical trial enrichment.

Abstract: A method, a program, and a method determining hypermutated type cancer with higher accuracy than before is provided.

Abstract: A method for identifying candidate target cells within a biological fluid specimen includes a digital image of the biological fluid specimen with the digital image having a plurality of color channels, identifying first connected regions of pixels of a minimum first intensity in a first channel, identifying second connected regions of pixels of a minimum second intensity in a second channel, and determining first connected regions and second connected regions that spatially overlap. For a pair of a first connected region and a second connected region that spatially overlap, whether the second connected region overlaps the first connected region by a threshold amount is determined, and if the second connected region overlaps the first connected region by the threshold amount then the portion of the image corresponding to the overlap is continued to be treated as a candidate for classification.

Abstract: There is provided an imaging system. The imaging system comprising a multispectral camera configured to capture a multispectral image of an object, an RGB camera configured to capture a color image of the object, at least one storage device configured to store spectrum information for each of a plurality of labeled objects, and processing circuitry. The processing circuitry is configured to determine, based on the captured multispectral image, spectrum information associated with the object, associate, based at least in part, on the spectrum information associated with the object and the stored spectrum information for each of the plurality of objects, a label with the color image of the object, and store, on the at least one storage device, the color image and the associated label as training data.

Abstract: A system for determining the viability of an embryo comprises an imaging device, an excitation device configured to direct an excitation energy at an embryo, a controller communicatively connected to the imaging device and the excitation device, configured to drive the excitation device and collect images from the imaging device at an imaging frequency, a processor performing steps comprising acquiring a set of images from the imaging device, performing a Fourier Transformation to generate a set of phasor coordinates, computing a D-trajectory, computing a set of values of additional parameters, comparing the set of values to a set of stored values related to embryos of known viability, and calculating a viability index factor of the embryo from the set of values and the set of stored values. Methods of calculating embryo viability and determining one or more properties of a tissue are also described.

Abstract: Techniques for identifying and enumerating candidate target cells within a biological fluid specimen are described. A digital image of the biological fluid specimen is received, and one or more candidate regions of pixels in the digital image are identified by identifying connected regions of pixels of a minimum intensity having a size between a minimum size and a maximum size and an aspect ratio that meets a threshold. For each candidate region of at least one of the one or more candidate region, whether the portion of the image corresponding to the candidate region includes more than a threshold number of intensity levels is determined. If the portion of the image corresponding to the candidate region includes more than the threshold number of intensity levels the portion of the image is continued to be treated as a candidate for classification.

Abstract: A method for identifying and enumerating candidate target cells within a biological fluid specimen is described. The method includes obtaining a biological fluid specimen, preparing the biological fluid specimen by staining cell features in the biological fluid specimen, capturing a digital image having a plurality of color channels of the biological fluid specimen, and applying image analysis to the digital image. A computer program product for identifying candidate target cells within a biological fluid specimen is also described. The computer program comprises instructions to cause a processor to carry out the image analysis.

Abstract: Face image data is acquired and a face image is captured, and a difference between a face image indicated by the face image data and the face image that is captured or a candidate of the difference is detected on the basis of at least one of the face image indicated by the face image data that is acquired, and the face image that is captured. Guidance is acquired on the basis of the difference or the candidate of the difference which is detected, and an output unit is controlled to output the guidance.

Abstract: A remaining time calculation unit calculates, based on a notification waiting time indicating a time from when a feature region is recognized to when a notification of a recognition result of the feature region is started and a count time counted by a time count unit, a remaining time until the notification of the recognition result of the feature region is provided. A display control unit displays on a monitor remaining time notification information obtained based on at least the remaining time.

Abstract: Described here are systems and methods for generating and implementing a hybrid machine learning and mechanistic model to produce biological feature maps, or other measurements of biological features, based on an input of multiparametric magnetic resonance or other images. The hybrid model can include a combination of a machine learning model and a mechanistic model that takes as an input multiparametric MRI, or other imaging, data to generate biological feature maps (e.g., tumor cell density maps), or other measures or predictions of biological features (e.g., tumor cell density). The hybrid models have capabilities of learning individual-specific relationships between imaging features and biological features.

Abstract: Anatomical and functional assessment of coronary artery disease (CAD) using machine learning and computational modeling techniques deploying methodologies for non-invasive Fractional Flow Reserve (FFR) quantification based on angiographically derived anatomy and hemodynamics data, relying on machine learning algorithms for image segmentation and flow assessment, and relying on accurate physics-based computational fluid dynamics (CFD) simulation for computation of the FFR.

Abstract: Described is a method for processing image data to determine if a portion of the image data is affected due to sunlight. In some implementations, image data is sent to an image data store and camera parameters are sent to a radiance detection service. The radiance detection service, upon receiving the camera parameters, retrieves the image data, converts the image data to gray-scale and processes the image data based on the camera parameters to determine a radiance value for the camera. The radiance value may be compared to a baseline radiance value to determine if sunlight is represented in the image data. In some implementations, a baseline model may be developed for the camera and used to cancel out any pixels of the image data that are overexposed under normal or baseline conditions. Likewise, a foreground model may be generated to detect any objects in the image data for which corresponding pixel values should not be considered for determining if sunlight is represented in the image data.

Abstract: A current observation area is determined exploratorily from among a plurality of candidate areas, on the basis of a plurality of observed areas in a biological tissue. A plurality of reference images obtained by means of low-magnification observation of the biological tissue are utilized at this time. A learning image is acquired by means of high-magnification observation of the determined current observation area. A plurality of convolution filters included in an estimator can be utilized to evaluate the plurality of candidate areas.

Abstract: A system performs cerebral hematoma analysis. The system includes a computing device receiving computerized tomography (CT) images from CT imaging devices. The CT images are associated with patients exhibiting cerebral hematomas. CT images may be converted into feature vectors and passed as input to a convolution neural network model for identification and diagnosis of hematoma volume changes. Detected changes may be thresholded to determine if the change represents an increase or shrinkage in the volumetry of the hematoma.

Abstract: A method of generating a quantitative characterization of injury presence and status of spinal cord tissue using an adaptive CNN system for use in diagnostic assessment, surgical planning, and therapeutic strategy comprises preprocessing for artifact correction of diffusion based, spinal cord MRI data, training an adaptive CNN system with healthy and abnormal (injured/pathologic) spinal cord images obtained by imaging a population of healthy, typically developed spinal cord subjects and subjects with spinal cord injury, evaluating a novel, diffusion-based MRI image for injury biomarkers using the adaptive CNN system, generating a three-dimensional predictive axonal damage map for quantitative characterization and visualization of the novel, diffusion-based MRI image, and transmitting the sets of healthy and injured spinal cord images back to a central database for continued improvement of the adaptive CNN system training. A system for defining a predictive spinal axonal damage map is also described.

Abstract: Described herein is medical imaging technology for concurrent and simultaneous synthesis of a medical CA-free-AI-enhanced image and medical diagnostic image analysis comprising: receiving a medical image acquired by a medical scanner in absence of contrast agent enhancement; providing the medical image to a computer-implemented machine learning model; concurrently performing a medical CA-free-AI-enhanced image synthesis task and a medical diagnostic image analysis task with the machine learning model; reciprocally communicating between the image synthesis task and the image analysis task for mutually dependent training of both tasks. Methods and systems and non-transitory computer readable media are described for execution of concurrent and simultaneous synthesis of a medical CA-free-AI-enhanced image and medical diagnostic image analysis.

Abstract: A method and a system automatically generate a digital representation of an annulus structure of a valve from a segmented digital representation of a human internal heart. The basis for the segmented digital representation is multi-slice computed tomography image data. The method includes automatically determining, for at least a first effective time point, based on a segmentation, i.e. labels, of a provided input segmented digital representation, a candidate plane, and/or a candidate orientation vector together with a candidate center point, arranged with respect to the input segmented digital representation for the first effective time point, and candidate points for the annulus structure are determined automatically. From the candidate points acting as support points, a candidate spline interpolation is generated which is then adapted based on the input segmented digital representation.

Abstract: An apparatus for classifying a brain tissue area as functional or non-functional by a stimulation of the brain includes a receiver unit for receiving information about a performed stimulation, a recording device for recording images that represent the brain tissue area, a detection unit for detecting a change in perfusion in the brain tissue area, and a classification unit configured to determine with the information whether there is a correlation between the performed stimulation and the detected change in perfusion, and to classify the brain tissue area as functional or as non-functional. The recording device is an endomicroscope for recording endomicroscopic images of the brain tissue area with a spatial resolution better than 20 ?m and a frame rate of at least 0.4 frames per second. The detection unit is configured to detect a change in perfusion based on the positions of certain tissue structures in the recorded images.

Abstract: The present disclosure is directed to a computer system designed to (i) receive a series of images as input; (ii) compute a number of metrics derived from focus features and color separation features within the images; and (iii) evaluate the metrics to return (a) an identification of the most suitable z-layer in a z-stack, given a series of z-layer images in a z-stack; and/or (b) an identification of those image tiles that are more suitable for cellular based scoring by a medical professional, given a series of image tiles from an area of interest of a whole slide scan.

Abstract: The present application relates to the cultivation of crop plants using plant protection agents. Certain embodiments relate to methods, a system, and a computer program product for determining partial-area-specific requirements of a crop plant for plant protection agents.