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.

In a magnetic resonance fingerprinting method and apparatus for improved determination of local parameter values of an examination object, in which at least two signal comparisons of acquired picture element time series are carried out with comparison signal curves for determination of parameter values. A further (subsequent) signal comparison takes into account results of a preceding signal comparison. This multi-stage determination of parameter values allows an increase of the spatial resolution and the precision with which the parameter values can be determined.

Some methods of analyzing one or more brain lesions of a patient comprise, for each of the lesion(s), calculating one or more lesion characteristics from a first 3-dimensional (3D) representation of the lesion obtained from data taken at a first time and a second 3D representation of the lesion obtained from data taken at a second time that is after the first time. The characteristic(s) can include a change, form the first time to the second time, in the lesion's volume and/or surface area, the lesion's displacement from the first time to the second time, and/or the lesion's theoretical radius ratio at each of the first and second times. Some methods comprise characterizing whether the patient has multiple sclerosis and/or the progression of multiple sclerosis in the patient based at least in part on the calculation of the lesion characteristic(s) of each of the lesion(s).

Methods, systems, and computer readable media are provided for processing medical images. One or more prior medical images are aligned with a current medical image. Image subtraction between the current medical image and the one or more prior medical images is performed to produce one or more difference images. The one or more difference images are applied to a machine learning model to determine a presence or an absence of a medical condition.

Disclosed is a technique for automatically performing disease detection using image processing. The technique includes receiving, from image capture devices, a first image depicting a first set of plants of a first unit and a second image depicting a second set of plants of a second unit. One or more metrics associated with the first and second sets of the plants are measured based at least on the images. At least one difference in the first and second sets of the plants is detected based at least on differences in the measurement for the one or more metrics associated with the first set of the plants and the second set of the plants. In response to detecting the at least one difference, additional images of the plants are requested from the one or more image capture devices to detect the presence of plant disease.

The subject matter discussed herein relates to the automatic, real-time registration of pre-operative magnetic resonance imaging (MRI) data to intra-operative ultrasound (US) data (e.g., reconstructed images or unreconstructed data), such as to facilitate surgical guidance or other interventional procedures. In one such example, brain structures (or other suitable anatomic features or structures) are automatically segmented in pre-operative and intra-operative ultrasound data. Thereafter, anatomic structure (e.g., brain structure) guided registration is applied between pre-operative and intra-operative ultrasound data to account for non-linear deformation of the imaged anatomic structure. MR images that are pre-registered to pre-operative ultrasound images are then given the same nonlinear spatial transformation to align the MR images with intra-operative ultrasound images to provide surgical guidance.

According to an exemplary embodiment of the present invention, a non-face-to-face consulting system for skin care and cosmetics use is provided that includes: a non-face-to-face consulting server providing a user with consultations on skin care method and recommended cosmetics based on skin conditions of an analyzed user by analyzing the skin conditions of the user when the skin condition information of the user is received; and a skin care application (App) disposed on a user terminal and transmitting the skin care information including a skin photograph of the user and a skin condition reply of the user to the non-face-to-face consulting server.

Disclosed herein is a method for detecting retinal pathologies in three dimensions using structural and angiographic OCT. The method in accordance with the present disclosure may operate by detecting deviations in reflectance and perfusion from a depth-normalized standard retina created by merging and averaging scans from healthy subjects. In one example, the deviations from the standard retina highlight key pathologic features, while depth-normalization obviates the need to segment retinal layers. Additionally, a composite pathology index is disclosed herein that measures average deviation from the standard retina. The present method is amenable to automation and may be implemented in an integrated system and/or provided in the form of software encoded on a computer-readable medium.

Systems and methods for detecting complex networks in MRI image data in accordance with embodiments of the invention are illustrated. One embodiment includes an image processing system, including a processor, a display device connected to the processor, an image capture device connected to the processor, and a memory connected to the processor, the memory containing an image processing application, wherein the image processing application directs the processor to obtain a time-series sequence of image data from the image capture device, identify complex networks within the time-series sequence of image data, and provide the identified complex networks using the display device.

This application describes a microfluidic system for measuring blood flow velocity using particle image velocimetry (PIV) and wavelet-based optical flow velocimetry (wOFV) processing to determine one or more hemodynamic parameters. The hemodynamic parameters can include whole blood rheology as well as spatiotemporal variations in blood velocity, respectively, during coagulation in flowing blood samples. The system can provide quantitative information on the formation and evolution of thrombi, identify subjects with clotting disorders associated with abnormal thrombus growth rate, and/or determine the effect of therapeutics and/or therapeutical approaches on clotting dynamics and thrombus formation.