An imaging system and method for microbial growth detection, counting or identification. One colony may be contrasted in an image that is not optimal for another type of colony. The system and method provides contrast from all available material through space (spatial differences), time (differences appearing over time for a given capture condition) and color space transformation using image input information over time to assess whether microbial growth has occurred for a given sample.

Devices and methods for labeling and mounting suspended cells in a controllable area are disclosed. The devices and methods utilize polycarbonate filters. The filters are employed both to capture the cells and as a substrate for labeling. This disclosure provides a device for cell capture and staining. This device utilizes a stack comprising a filter sandwiched between two o-rings (an “OFO stack”) in which the o-rings both seat the device and, based on their outer diameter and cross-section, determine the cell capture area. In one embodiment, an alignment plate is affixed to an output head of the device, the alignment plate having one or more through holes, a diameter of the one or more through holes matching an outer diameter of the OFO stack.

The disclosed technology relates to an inspection apparatus that includes a stage configured to retain a specimen for inspection, an imaging device having a field of view encompassing at least a portion of the stage to view a specimen retained on the stage, and a plurality of lights disposed on a moveable platform. The inspection apparatus can further include a control module coupled to the imaging device, each of the lights and the moveable platform. The control module is configured to perform operations including: receiving image data from the imaging device, where the image data indicates an illumination landscape of light incident on the specimen; and automatically modifying, based on the image data, an elevation of the moveable platform or an intensity of one or more of the lights to adjust the illumination landscape. Methods and machine-readable media are also contemplated.

Provided are a morphological cell parameter-based erythrocyte test method and digital holographic microscope used therein, and the morphological cell parameter-based erythrocyte test method includes performing modeling to create a 3D image of an erythrocyte to be tested and measuring morphological parameters of the erythrocyte based on the 3D image.

The morphological cell parameter-based erythrocyte test method performs modeling of a 3D image for an erythrocyte to be tested and measures morphological parameters of the erythrocyte based on the 3D image. Therefore, time and effort consumed in measurement may be reduced, and accuracy of the measurement is excellent.

Disclosed is a method of measuring red blood cell membrane fluctuations based on dynamic cell parameters using a digital holographic microscope; the method including a step of modeling the three-dimensional images of red blood cells to be measured, and a step of measuring red blood cell membrane fluctuations based on the three-dimensional images. According to this method, since the three-dimensional images of red blood cells to be measured are modeled and red blood cell membrane fluctuations are measured based on the three-dimensional images, red blood cell membrane fluctuations can be measured more easily.

An apparatus for obtaining a plurality of images of a sample includes a sample device suitable for holding a liquid sample; a first optical detection assembly including a first image acquisition device, the first optical detection assembly having an optical axis and an object plane, the object plane including an image acquisition area from which electromagnetic waves can be detected as an image by the first image acquisition device; one translation unit arranged to move the sample device and the first optical detection assembly relative to each other; and an image illumination device, wherein the apparatus is arranged to move the sample device and the first optical detection assembly relative to each other along a scanning path, which defines an angle theta relative to the optical axis, wherein theta is in the range of about 0.3 to about 89.7 degrees.

There is provided systems and methods for generating 3D structure estimation of at least one target from a set of 2D Cryo-electron microscope particle images. The method includes: receiving the set of 2D particle images of the target from a Cryo-electron microscope; splitting the set of particle images into at least a first half-set and a second half-set; iteratively performing: determining local resolution estimation and local filtering on at least a first half-map associated with the first half-set and a second half-map associated with the second half-set; aligning 2D particles from each of the half-sets using at least one region of the associated half-map; for each of the half-maps, generating an updated half-map using the aligned 2D particles from the associated half-set; and generating a resultant 3D map using all the half-maps.

A system and method that identify and classify unknown microorganisms and/or known microorganisms with anomalies are provided. The system and method comprise processing images of microorganisms from an aquatic environment; extracting features from the processed images; an unsupervised partitioning algorithm for identifying and classifying known microorganisms in the aquatic environment based upon the extracted features; and a supervised classifier neural network that is trained with the unsupervised partitioning algorithm and identifies and classifies unknown microorganisms and/or known microorganisms with anomalies.

Methods, apparatus, and other embodiments predict tumor infiltrating lymphocyte (TIL) density from pre-surgical computed tomography images of a region of tissue demonstrating non-small cell lung cancer (NSCLC). One example apparatus includes a set of circuits that includes an image acquisition circuit that accesses a radiological image of a region of tissue demonstrating cancerous pathology, where the radiological image has a plurality of pixels, and where the radiological image includes an annotated region of interest (ROI), a feature extraction circuit that extracts a set of radiomic features from the ROI, where the set of radiomic features includes at least two texture features and at least one shape feature, and a classification circuit that comprises a machine learning classifier that classifies the ROI as high tumor infiltrating lymphocyte (TIL) density, or low TIL density, based, at least in part, on the set of radiomic features.

The disclosure relates to systems and method for processing images. The method includes selecting a predetermined reference structure, the predetermined reference structure having a known feature size/shape. The method also includes obtaining a reference image of the predetermined reference structure, and capturing a calibration image of the predetermined reference structure using an observation device. The calibration image includes a plurality of features. Additionally, the method includes identifying at least one portion of the plurality of features of the calibration image that include a feature size/shape substantially similar to the known feature size and shape of the predetermined reference structure. Finally, the method includes combining the identified portion of the plurality of features of the calibration image to form a stacked feature image, and determining a point spread function (PSF) of the observation device by comparing the obtained reference image with the stacked feature image.