Provided herein are embodiments of methods and systems for screening cellular, subcellular, and multicellular structures. In one embodiment, a method for screening is provided comprising the steps of introducing a plurality of cellular, subcellular, or multicellular structures, or a combination thereof, to an imaging system, wherein one or more structures of the plurality comprise one or more taggable markers; imaging the plurality of structures using the imaging system; identifying one or more target structures among the plurality of structures based on one or more properties of the target structures; tagging the target structures to produce tagged target structures, wherein each target structure is selectively illuminated by an excitation light, thereby causing one or more taggable markers within the target structure to be phototransformed to produce one or more phototransformed taggable markers within the target structure; and isolating one or more tagged target structures from the plurality of structures.

The invention relates to a monitoring apparatus configured to monitor a processing status of a food item under processing in a food processor, the monitoring apparatus comprising a sensor operable to determine characteristic information related to the food item in the food processor, a controller configured to provide a control signal to the food processor to control an operation of the food processor when the determined characteristic 5 information or a rate of change of the determined characteristic information meets a predetermined criteria.

A computing device, method, system, and instructions in a non-transitory computer-readable medium for performing image analysis on 3D microscopy images to predict localization and/or labeling of various structures or objects of interest, by predicting the location in such images at which a dye or other marker associated with such structures would appear. The computing device, method, and system receives sets of 3D images that include unlabeled images, such as transmitted light images or electron microscope images, and labeled images, such as images captured with fluorescence tagging. The computing device trains a statistical model to associate structures in the labeled images with the same structures in the unlabeled light images. The processor further applies the statistical model to a new unlabeled image to generate a predictive labeled image that predicts the location of a structure of interest in the new image.

Embodiments of the present disclosure provide for improved generation and outputting of object identification data indicating object classifications for object representations. Such objects representations may correspond to depictions of objects in images captured using digital holographic microscopy. Some embodiments generate object identification data by comparing object representations in focused image(s) with specially configured annotated focused images, for example using a specially trained neural network or other machine learning model trained based on such annotated focused images. The annotated focused images are generated including a plurality of channels, each associated with a different grayscale focused image at a different target focal length of a range of target focal lengths. In this regard, model(s), algorithm(s), and/or other specially configured implementations may learn the spatial features of particular object representations and associated object identification data. The trained models may be used to perform accurate comparisons with the annotated focused images.

The method of the present invention classifies the nuclei in prostate tissue images with a trained deep learning network and uses said nuclear classification to classify regions, such as glandular regions, according to their malignancy grade. The method according to the present disclosure also trains a deep learning network to identify the category of each nucleus in prostate tissue image data, said category representing the malignancy grade of the tissue surrounding the nuclei. The method of the present disclosure automatically segments the glands and identifies the nuclei in a prostate tissue data set. Said segmented glands are assigned a category by at least one domain expert, and said category is then used to automatically assign a category to each nucleus corresponding to the category of said nucleus' surrounding tissue. A multitude of windows, each said window surrounding a nucleus, comprises the training data for the deep learning network.

In some embodiments, a process and system are provided for generating a user interface for classification of a sample image of a cell that includes receiving a sample image of a sample particle from a biological sample and selecting reference images that each portray a reference particle of a biological sample. The reference images can be ordered based on similarity and the reference images can be selected based on the order. The first selected reference image can be aligned with the sample image and expanded such that the adjacent edges of the reference image and sample image are the same. The expanded image can be dynamically filled. The sample image and the expanded reference image can be displayed in a user interface.

A system for characterizing at least one particle from a fluid sample is disclosed. The system includes a filter disposed upstream of an outlet, and a luminaire configured to illuminate the at least one particle at an oblique angle. An imaging device is configured to capture and process images of the illuminated at least one particle as it rests on the filter for characterizing the at least one particle. A system for characterizing at least one particle using bright field illumination is also disclosed. A method for characterizing particulates in a fluid sample using at least one of oblique angle and bright field illumination is also disclosed.

The invention relates to a method for producing at least one pattern (2) on a carrier surface (7) of a carrier (3), wherein the method comprises the following steps: a. adding a first fluid (4) to the carrier surface (7) and b. adding a second fluid (5), wherein the second fluid (5) is immiscible with the first fluid (4) and at least partially covers the first fluid (4) and c. adding at least one object (6) above the carrier surface (7), d. generating the pattern (2) by a relative movement between the object (6) and the carrier (3) in which a force is exerted on the object (6) from a force generating means (28), wherein the force transmission from the force generating means (28) on the object (6) is contactless and the pattern (2) is generated by a portion of the second fluid (15) wetting the carrier surface (7).

The method can include: acquiring an image of an arrangement having at least one cell covering at least one adhesive surface area, the at least one adhesive surface area being predictively deformable upon exerting cellular forces stemming from said at least one cell; measuring, from the image, a surface area of the at least one cell; comparing the measured surface area to a reference surface area value; and generating a signal indicative of the cellular forces based on said comparison.

A device for detecting particles in air; said device comprising: a flow channel configured to allow a flow of air comprising particles through the flow channel; a light source configured to illuminate the particles, such that an interference pattern is formed by interference between light being scattered by the particles and non-scattered light from the light source; an image sensor configured to detect incident light, detect the interference pattern, and to acquire a time-sequence of image frames, each image frame comprising a plurality of pixels, each pixel representing a detected intensity of light; and a frame processor configured to filter information in the time-sequence of image frames, wherein said filtering comprises:
identifying pixels of interest in the time-sequence of image frames, said pixels of interest picturing an interference pattern potentially representing a particle in the flow of air, and outputting said identified pixels of interest for performing digital holographic reconstruction.