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. Distribution systems and methods described may track and control the distribution of an aquatic plant culture based on information received from various sources. System and methods described may include a bioreactor having a plurality of vertically stacked modules designed to contain the aquatic plants and a liquid growth medium.

Devices, systems, and methods for visually depicting a vessel and evaluating a physiological condition of the vessel are disclosed. One embodiment includes obtaining, at a first time, a first image of the vessel, the image being in a first medical modality, and obtaining, at a second time subsequent to the first time, a second image of the vessel, the image being in the first medical modality. The method also includes spatially co-registering the first and second images and outputting a visual representation of the co-registered first and second images on a display. Further, the method includes determining a physiological difference between the vessel at the first time and the vessel at the second time based on the co-registered first and second images, and evaluating the physiological condition of the vessel of the patient based on the determined physiological difference.

Systems and methods are disclosed to measure a PPG signal. In some embodiments, a method may include capturing a plurality of frames of a subject; tracking the position of a region of interest of the subject in each of the plurality of frames; creating a first time series signal, a second time series signal, and third time series signal corresponding with respective color channels of the plurality of frames; normalizing the first, second, and third time series signals, combining the normalized first time series signal, the normalized first time series signal, and the normalized first time series signal into a combined signal; creating a spectral signal from the combined signal; and extracting the PPG signal from the spectral signal.

Systems and methods for producing ultrasound images are disclosed herein. In one embodiment, ultrasound image data are acquired in discrete time increments at one or more positions relative to a subject. Control points are added by a user for two or more image frames and a processor interpolates the location of the control points for image frames obtained at in-between times.

A method for characterising motion of one or more objects in a time ordered image dataset comprising a plurality of time ordered data frames, the method comprising: selecting a reference data frame from the plurality of time ordered data frames (210); extracting a plurality of image patches from at least a part of the reference data frame (220); identifying a location of each image patch of at least a subset of the plurality of image patches in each data frame (230); defining, based on the identified locations, a mesh for each data frame, wherein vertices of each mesh correspond to respective identified locations of image patches in the corresponding data frame (240); and deriving, from the meshes, a motion signature for the time ordered image dataset, the motion signature characteristic of the motion of the one or more objects in the plurality of time ordered data frames (250).

The invention relates to a motion determination device for determining the motion of an object. The motion determination device comprises a magnetic resonance (MR) information providing unit (2, 5) for providing an MR image of the object (6) and for providing non-image MR data of the object which have been acquired at different acquisition times, and a motion determination unit (9) for determining a motion field, which describes the motion of the object (6), depending on the provided non-image MR data acquired at the different acquisition times and the provided MR image. Since the non-image MR data, which are preferentially k-space data, are directly used for determining the motion field, i.e. without an intermediate reconstruction of MR images based on the non-image MR data, the motion field can be determined with a very high temporal resolution.

A method for tracking movement of nematode worms comprises: (i) providing a plurality of worms on a translucent substrate; (ii) obtaining a first image of a field of view including the plurality of worms by transmission imaging; (iii) obtaining a first difference image of the plurality of worms corresponding to an intensity difference between said first image and a background image of the field of view; (iv) repeating the following steps (a) to (d) a plurality of N times, for n=1 to N: (a) determining, from the first difference image, an nth pixel corresponding to a maximum intensity difference; (b) selecting, from the first difference image, an nth block of pixels comprising the selected nth pixel; (c) determining a coordinate associated with the selected nth block of pixels; and (d) updating said first difference image by setting each pixel of said nth block of pixels in said first difference image to a value corresponding to a zero or low intensity difference; (v) obtaining a sequence of M subsequent images of the field of view by transmission imaging; and (vi) repeating the following steps (f) and (g) for each of the M subsequent images, for m=2 to m=M+1: (f) obtaining an mth difference image of the plurality of worms corresponding to an intensity difference between the mth subsequent image and said background image; and (g) repeating the following steps a plurality of N times, for n=1 to n=N: determining, from the mth difference image, an nth pixel corresponding to a maximum intensity difference or a centre of the intensity difference distribution of a trial block of pixels positioned at the determined coordinate associated with the corresponding nth selected block of pixels of the (m−1)th difference image; selecting an nth block of pixels of said mth difference image, said nth block of pixels comprising the determined nth pixel; and determining a coordinate associated with the selected nth block of pixels of said mth difference image.

A radiographic imaging apparatus (100) is configured to generate movement maps (30) of pixels (21) belonging to a first image (11) based on the first image (11) and a second image (12) captured at different times, to move a pixel (21) of the first image (11) based on a smoothed movement map (30a) in which high-frequency components of the movement maps (30) have been suppressed in a spatial direction and generate a deformed image (11a), and to combine the deformed image (11a) and the second image (12).

Disclosed are methods, systems, and articles of manufacture for performing a process on biological samples. An analysis of biological samples in multiple regions of interest in a microfluidic device and a timeline correlated with the analysis may be identified. One or more region-of-interest types for the multiple regions of interest may be determined; and multiple characteristics may be determined for the biological samples based at least in part upon the one or more region-of-interest types. Associated data that respectively correspond to the multiple regions of interest in a user interface for at least a portion of the biological samples in the user interface based at least in part upon the multiple identifiers and the timeline. A count of the biological samples in a region of interest may be determined based at least in part upon a class or type of data using a convolutional neural network (CNN).

In certain embodiments, a system for measuring the posterior corneal surface of the cornea comprises cameras and a computer. Each camera generates image data representing a part of the eye posterior to the cornea. The image data describes locations of features of the part. The computer stores a description of the shape of an anterior corneal surface of the cornea, and applies a ray-tracing process to determine the shape of the posterior corneal surface. The ray-tracing process comprises defining rays, where each ray is traced from a camera, through the anterior and posterior corneal surfaces, and to the part of the eye. Constraints for the rays are determined, where the constraints are calculated using the description of the shape of the anterior corneal surface and locations of the features in the image data. Parameters are optimized, and the optimized parameters describe the shape of the posterior corneal surface.