A method include obtaining at least one first PET image of a subject acquired by a PET scanner and at least one first MR image of the subject acquired by an MR scanner. The method may also include obtaining a target neural network model. The target neural network model may provide a mapping relationship between PET images, MR images, and corresponding attenuation correction data, and output attenuation correction data associated with a specific PET image of the PET images. The method may further include generating first attenuation correction data corresponding to the subject using the target neural network model based on the at least one first PET image and the at least one first MR image of the subject, and determining a target PET image of the subject based on the first attenuation correction data corresponding to the subject.

Implementations relate to detecting/replacing transient obstructions from high-elevation digital images, and/or to fusing data from high-elevation digital images having different spatial, temporal, and/or spectral resolutions. In various implementations, first and second temporal sequences of high-elevation digital images capturing a geographic area may be obtained. These temporal sequences may have different spatial, temporal, and/or spectral resolutions (or frequencies). A mapping may be generated of the pixels of the high-elevation digital images of the second temporal sequence to respective sub-pixels of the first temporal sequence. A point in time at which a synthetic high-elevation digital image of the geographic area may be selected. The synthetic high-elevation digital image may be generated for the point in time based on the mapping and other data described herein.

Implementations relate to crop yield prediction at the field- and pixel-level. In various implementations, a first temporal sequence of high-elevation digital images may be obtained that capture a first geographic area and are acquired over a first predetermined time interval while the first geographic area includes a particular crop. A first plurality of other data points may also be obtained that influence a ground truth crop yield of the first geographic area after the first predetermined time interval. The first plurality of other data points may be grouped into temporal chunks corresponding temporally with respective images of the first temporal sequence. The first temporal sequence and the temporal chunks of the first plurality of other data points may be applied, e.g., iteratively, as input across a machine learning model to estimate a crop yield of the first geographic area at the end of the first predetermined time interval.

Implementations relate to diagnosis of crop yield predictions and/or crop yields at the field- and pixel-level. In various implementations, a first temporal sequence of high-elevation digital images may be obtained that captures a geographic area over a given time interval through a crop cycle of a first type of crop. Ground truth operational data generated through the given time interval and that influences a final crop yield of the first geographic area after the crop cycle may also be obtained. Based on these data, a ground truth-based crop yield prediction may be generated for the first geographic area at the crop cycle's end. Recommended operational change(s) may be identified based on distinct hypothetical crop yield prediction(s) for the first geographic area. Each distinct hypothetical crop yield prediction may be generated based on hypothetical operational data that includes altered data point(s) of the ground truth operational data.

Frames of a video frame sequence capturing one or more skin regions of a body are provided to a first neural network. The first neural network generates respective appearance representations based on the frames. An appearance representation generated based on a particular frame is indicative of a spatial distribution of a physiological signal across the particular frame. Simultaneously with providing the frames to the first neural network, the frames are also provided to a second neural network. The second neural network determines the physiological signal based on the frames. Determining the physiological signal by the second neural network includes applying the appearance representations, generated by the first neural network, to outputs of one or more layers of the second neural network to emphasize regions, in the frames, that exhibit relatively stronger presence of the physiological signal and deemphasize regions, in the frames, that exhibit relatively weaker presence of physiological signal.

Disclosed herein are methods for predicting the reprogramming process of cells from a microscopic image of one or more cells. According to some embodiments, the method includes capturing an image of region of interest (ROI) of every pixel of the microscopic image, followed by processing the ROI image with a trained convolutional neural network (CNN) model and a trained long short-term memory (LSTM) network so as to obtain predicted probability maps. Also disclosed herein are a storage medium and a system for executing the present methods.

Systems and methods are provided for automated analysis of angiographic images. An angiographic imaging system is configured to capture a first image of a region of interest, representing a first time, and a second image of a region of interest, representing a second time. A registration component is configured to register the first image to the second image. A difference component is configured to generate a difference image from the first image and the second image. A pattern recognition component is configured to assign a clinical parameter to the region of interest from the difference image and at least one of the first image and the second image.

System and methods for monitoring the growth of an aquatic plant culture and detecting real-time characteristics associated with the aquatic plant culture aquatic plants. The systems and methods may include a control unit configured to perform an analysis of at least one image of an aquatic plant culture. The analysis may include processing at least one collected image to determine at least one physical characteristic or state of an aquatic plant culture. Systems and methods for distributing aquatic plant cultures are also provided. The distribution systems and methods may track and control the distribution of an aquatic plant culture based on information received from various sources. Systems and methods for growing and harvesting aquatic plants in a controlled and compact environment are also provided. The systems may include a bioreactor having a plurality of vertically stacked modules designed to contain the aquatic plants and a liquid growth medium.

A cell survival rate determining device includes a distribution acquisition unit acquiring a particle size distribution in a solution containing particles including cultured cells; a classification unit classifying the particles into a small particle group and a large particle group using a predetermined threshold in the distribution; a ratio calculation unit calculating the value of a ratio between the number of particles belonging to the small particle group and the number of particles belonging to the large particle group; and a survival rate determination unit determining the survival rate of the cultured cells from the value of the ratio using a pre-acquired relationship between the ratio and cell survival rate.

According to some aspects, an image processing apparatus is provided. The image processing apparatus includes circuitry configured to receive at least two images of a biological sample and determine motion information for a plurality of regions of the at least two images. The motion information corresponds to motion of the biological sample. The circuitry is further configured to generate a graphical representation of at least two characteristic amounts. The at least two characteristic amounts correspond to a region of the plurality of regions and one characteristic amount of the at least two characteristic amounts is indicative of the motion information.