Micro-biological colony counters and more particularly, to a device and a method for lighting, conditioning and capturing image(s) of organic sample(s) such as but not limited to micro-organisms. The device (700) captures accurate image(s) of organic sample(s) and has a fixed focus imaging for repeatability in quality of images. The device (700) can capture images of organic sample in different lighting and color conditions thereby improving detection of microbiological colonies by increasing the contrast from the background medium. The color calibrated imaging device (700) provides diffused illumination by using polychromatic LED lights, light reflectors and light diffusers for optimal color reproduction of micro-biological colonies contained in organic sample(s). The device (700) is adapted for automatic capturing of images of organic sample(s) cultivated on petri dishes of different sizes.

An imaging system and method provides automated microbial growth detection for antibiotic sensitivity testing. A processing system having an image sensor for capturing images of an inoculated culture plate having antibiotic disks disposed on the culture media captures images of the plate at separate times (e.g., first and second images). The system generates pixel characteristic data for pixels of the second image from a comparison of the first image and second image. The pixel characteristic data may be indicative of plate growth. The system may access growth modeling data concerning the antibiotic disk(s) and generate simulated image data with a growth model function. The growth model function uses the growth modeling data. The simulated image data simulates growth on the plate relative to the disk(s). The system compares the simulated image and the pixel characteristic data to identify pixel region(s) of the second image that differ from the simulated image.

Systems and methods for imaging cells. Quantitative phase imaging uses variations in the index of refraction of a sample as a source of endogenous contrast, providing label-free information of sub-cellular structures and allowing for the reconstruction of valuable biophysical parameters, such as cell dry-mass at femtogram scales, mass transport, and sample thickness and fluctuations at nanometer scales. As a result, QPI has become a valuable tool in biology and medicine. However, QPI has suffered from the need for trans-illumination through relatively thin objects in order to gain access to the forward-scattered field, which carries crucial low spatial frequency information of a sample and avoid contributions from multiple scattered light or out-of-focus planes. The disclosed methods and systems can provide for reconstruction of QPI and corresponding analysis for imaging samples of cells in thick samples using an epi-illumination configuration.

The present application relates generally to identifying regions of interest in images, including but not limited to whole slide image region of interest identification, prioritization, de-duplication, and normalization via interpretable rules, nuclear region counting, point set registration, and histogram specification color normalization. This disclosure describes systems and methods for analyzing and extracting regions of interest from images, for example biomedical images depicting a tissue sample from biopsy or ectomy. Techniques directed to quality control estimation, granular classification, and coarse classification of regions of biomedical images are described herein. Using the described techniques, patches of images corresponding to regions of interest can be extracted and analyzed individually or in parallel to determine pixels correspond to features of interest and pixels that do not. Patches that do not include features of interest, or include disqualifying features, can be disqualified from further analysis. Relevant patches can analyzed and stored with various feature parameters.

Apparatus and methods are described for analyzing a bodily sample. A microscope system acquires one or more microscope images of the bodily sample. A computer processor identifies elements as being candidates of a given entity, in the one or more images. At least one sample-informative feature, relating to a characteristic of the candidates of the given entity in the sample as a whole, is extracted from the one or more images. A characteristic of the sample is determined at least partially based upon the sample-informative feature, and an output is generated in response thereto. Other applications are also described.

Provided is a cell image analysis method which enables accurate grasping of a change point of a colony in cell culture. The cell image analysis method includes: a data acquisition step of acquiring cell image data generated in time series in the cell culture; a cell region extraction step of extracting cell regions from the cell image data; a data calculation step of calculating, for each of the cell regions, data about a size of the cell region; and a change point detection step of detecting, based on the data about the size of the cell region calculated in the data calculation step, the change point, which is timing of a change in a state of the colony.

Systems and methods of using the same for functional fluorescence imaging of live cells in suspension with isotropic three dimensional (3D) diffraction-limited spatial resolution are disclosed. The method-live cell computed tomography (LCCT)-in-volves the acquisition of a series of two dimensional (2D) pseudo-projection images from different perspectives of the cell that rotates around an axis that is perpendicular to the optical axis of the imaging system. The volumetric image of the cell is then tomographically reconstructed.

Systems and methods for autofocus using artificial intelligence include (i) capturing a plurality of monochrome images over a nominal focus range, (ii) identifying one or more connected components within each monochrome image, (iii) sorting the identified connected components based on a number of pixels associated with each connected component, (iv) generating a focus quality estimate of at least a portion of the sorted connected components using a machine learning module, and (iv) calculating a target focus position based on the focus quality estimate of the evaluated connected components. The calculated target focus position can be used to perform cell counting using artificial intelligence, such as by (i) generating a seed likelihood image and a whole cell likelihood image based on output—a convolutional neural network and (ii) generating a mask indicative quantity and/or pixel locations of objects based on the seed likelihood image.

A method of detecting a biological substance in a sample, comprises: illuminate the sample by light; imaging the illuminated sample by Fourier transform hyperspectral imaging; and analyzing the obtained hyperspectral image to detect the biological substance in a sample.

A system and method for analyzing bodily fluid include a sample holder holding a bodily fluid sample, an image capture device generating an image of the bodily fluid sample comprising a plurality of fields of view. An image processor is programmed to determine a biofilm in the bodily fluid sample from the image, determine a biofilm area or volume within each of the plurality of fields of view to form a plurality of biofilm areas, determine a total biofilm area or total biofilm volume by adding the plurality of biofilm areas, determine a first value corresponding to a comparison of the total biofilm area or the total biofilm volume and a total volume of the bodily fluid sample, and classify the first value into a classification. An analyzer, using the classification, displays an indicator on a display for indicating the classification of the biofilm within the bodily fluid sample.