A system and method to analyze image data. The image data may be used to assist in determine the presence of a feature in the image. The feature may include a bubble.

Disclosed herein are methods and systems for automated detection of shadow artifacts in optical coherence tomography (OCT) and/or OCT angiography (OCTA). The shadow detection includes applying a machine-learning algorithm to the OCT dataset and the OCTA dataset to detect one or more shadow artifacts in the sample. The machine-learning algorithm is trained with first training data from first training samples that include manufactured shadows and no perfusion defects and second training data from second training samples that include perfusion defects and no manufactured shadows. The shadow artifacts in the OCTA dataset and/or OCT dataset may be suppressed to generate a shadow-suppressed OCTA dataset and/or a shadow-suppressed OCT dataset, respectively. Other embodiments may be described and claimed.

Methods and systems for crop grading and crop management. One or more images of crops are obtained and one or more crop related features are at least one of identified or extracted from the one or more images. A crop health status is determined based on the one or more crop related features, an environmental context, a growth stage of the crop, and a farm cohort by using a computerized deep learning system to perform an automated growth stage analysis. One or more actions are at least one of recommended, triggered, and performed.

The invention relates to a method, an image processor (26) and a medical observation device (1), such as a microscope or endoscope, for observing an object (4) containing a bolus of at least one fluorophore (12). The object (4) is preferably live tissue comprising several types (16, 18, 20) of tissue. According to the method, a set (34) of component signals (36) is provided. Each component signal (36) represents a fluorescence intensity development of the fluorophore (12) over time in a different type of tissue. A time series (8) of input frames (10) is accessed, one input frame (10) after the other. The input frames (10) represent electronically coded still images of the object (4) at subsequent time. Each input frame (10) contains at least one observation area (22) comprising at least one pixel (23). In the observation area (22) of the current input frame (10) of the time series (8), a fluorescent light intensity (I) is determined over at least one fluorescence emission wavelength (15) of the fluorophore (12). This fluorescent light intensity (I.sub.1) is joined with the fluorescence light intensities (I.sub.n) of the observation area (22) of preceding input frames (10) of the time series (8) to generate a time sequence (40) of fluorescent light intensities (I.sub.1, I.sub.n) of the observation area (22). This time sequence (40) is decomposed on in a preferably linear combination (72) of at least some of the component signals (36) of the set (34). A new set (34) of component signals (36) is provided which includes only those component signals (36) which are present in the combination (72). An output frame (46) is generated, in which the observation area (22) is assigned a color from a color space depending on the combination (72) of component signals (36).

Systems and methods for use in identifying a brain condition of a subject are provided. In some aspects, a provided method includes constructing a classifier to identify a brain condition of a subject comprising steps of receiving image data obtained from a plurality of subjects, wherein the image data is acquired during an acquisition period following administration of at least one radioactive tracer. The method also includes defining a plurality of brain condition classes using the image data associated with one or more time frames during the acquisition period, and processing the image data to generate signatures corresponding to each of the plurality brain condition classes. The method further includes constructing the classifier using the signatures. The classifier can then be applied to determine a degree to which the subject expresses one or more disease states in order to determine a brain condition of the subject.

A medical scan comparison system is operable to receive a medical scan via a network and to generate similar scan data. The similar scan data includes a subset of medical scans from a medical scan database and is generated by performing an abnormality similarity function to determine that a set of abnormalities included in the subset of medical scans compare favorably to an abnormality identified in the medical scan. At least one cross-sectional image is selected from each medical scan of the subset of medical scans for display on a display device associated with a user of the medical scan comparison system in conjunction with the medical scan.

Devices and methods for preparing RNA and DNA from single cells are disclosed. In particular, the invention relates to a method of simultaneously extracting RNA and DNA from single cells and separating the nucleic acids electrophoretically. An electric field is used to lyse a single target cell, such that the plasma membrane is selectively disrupted without lysing the nuclear membrane. Cytoplasmic RNA is separated from the nucleus by performing isotachophoresis (ITP) in the presence of a sieving matrix that preferentially reduces the mobility of the nucleus. During ITP, the cytoplasmic RNA accumulates at an ITP interface between leading and trailing electrolytes and can later be extracted free of nuclear DNA. The method can be performed in a microfluidic device that fully automates all steps of the process.

A method for analyzing a physical condition based on at least two images. The method selects a plurality of images based on a time lapse interval associated with the plurality of images and a specific physical characteristic being monitored. The method further detects a degree of change in the specific physical characteristic being monitored, wherein the degree of change comprises a change in one or more pixel patterns of at least one image within the plurality of images correlated with the specific physical characteristic being monitored, based on the time lapse interval associated with the plurality of images. The method further displays an alert in response to the degree of change exceeding a prescribed threshold for the specific physical characteristic being monitored.

A method for analyzing a physical condition based on at least two images. The method selects a plurality of images based on a time lapse interval associated with the plurality of images and a specific physical characteristic being monitored. The method further detects a degree of change in the specific physical characteristic being monitored, wherein the degree of change comprises a change in one or more pixel patterns of at least one image within the plurality of images correlated with the specific physical characteristic being monitored, based on the time lapse interval associated with the plurality of images. The method further displays an alert in response to the degree of change exceeding a prescribed threshold for the specific physical characteristic being monitored.

A vehicle control device includes a tracking unit estimating a motion of a moving object, a model selection unit selecting a motion model corresponding to a moving object type, an abnormality determination unit determining a presence or absence of an abnormality of the estimation of the motion of the moving object based on the estimated moving object motion and the motion indicated by the motion model, and a control unit. A control mode in which the control unit controls traveling of a host vehicle when the abnormality determination unit determines that the abnormality is present differs from a control mode in which the control unit controls the traveling of the host vehicle when the abnormality determination unit determines that the abnormality is absent.