A method for analyzing a biochip image is provided, including: (S1) acquiring and preprocessing the biochip image to obtain a preprocessed image; (S2) performing a correction for angle deflection on the preprocessed image to obtain a deflection-corrected image; and (S3) performing an enhancement processing on the deflection-corrected image, and identifying a positive or negative of an area of interest in the preprocessed image according to an image on which the enhancement processing has been performed. An apparatus (100) for analyzing a biochip image, a method for analyzing an image, a computer device (200) and a storage medium are disclosed.

An analyte testing system for quantifying the presence of an analyte in a specimen by immunochromatography. The system comprises a camera test card, depicting a test cassette (10) with an immunochromatography and a handheld processor device (16) comprising a digital camera (16a), a source of light (16b) and a processor (16c), which software and hardware (16c) are configured to make a pose estimation of camera and object and the measures of light in the region of interest of the immunochromatography. The system allows an automatic camera calibration and certification as a scanner for use in point-of-care diagnostics.

The present invention relates to a sequencing method which allows for increased rates of sequencing and an increase in the density of sequencing data. The system may be based on next generation sequencing methods such as sequencing by synthesis (SBS) but uses multiple primers bound at different positions on the same nucleic acid strand.

The technology disclosed relates to equalizer-based intensity correction for base calling. In particular, the technology disclosed relates to accessing an image whose pixels depict intensity emissions from a target cluster and intensity emissions from additional adjacent clusters, selecting a lookup table that contains pixel coefficients that are configured to increase a signal-to-noise ratio, applying the pixel coefficients to intensity values of the pixels in the image to produce an output, and base calling the target cluster based on the output.

Described herein are methods, systems, and non-transitory computer-readable media to non-destructively acquire three-dimensional profiles of cellular microbiological samples growing on the surface of a solid growth medium. Acquisitions can be performed by an optical microscope that includes a vertical scanning interferometer. The three-dimensional profiles can enable measurement of sample parameters of microcolonies, which can be made of microbial colony forming units. The methods and systems enable early and rapid detection and quantification of microbes.

The technology is provided to effectively visualize culture statuses related to a plurality of culture targets. Provided is a control device including a display control unit that controls dynamic display related to a culture status of a culture target including a cell having a division potential, the culture status being estimated along a time series by morphological analysis using a learned model generated on the basis of a machine learning algorithm, in which the display control unit controls comparative display of the culture statuses of a plurality of the culture targets. Furthermore, provided is a control method including controlling, by a processor, dynamic display related to a culture status of a culture target including a cell having a division potential, the culture status being estimated along a time series by morphological analysis using a learned model generated on the basis of a machine learning algorithm, and controlling the display further including controlling comparative display of the culture statuses of a plurality of the culture targets.

A method for assessing an assessment target according to an embodiment includes: a step of acquiring statistical information based on at least one color feature quantity with respect to each of a plurality of assessment regions in a plate image corresponding to an image of an assessment plate that holds an assessment target in a plurality of wells provided in the plate; and a step of determining a color of the plurality of assessment regions by using the statistical information. The assessment target includes a tester, the plurality of wells include a test substance well holding the assessment target that further includes a test substance, the plate image includes a plurality of well images corresponding to the plurality of wells, and each of the plurality of assessment regions includes at least one well image corresponding to at least one of the wells.

In a computer-implemented method for generating an image processing model that generates output data defining a stylized contrast image from a microscope image, model parameters of the image processing model are adjusted by optimizing at least one objective function using training data. The training data comprises microscope images as input data and contrast images, wherein the microscope images and the contrast images are generated by different microscopy techniques. In order for the output data to define a stylized contrast image, the objective function forces a detail reduction or the contrast images are detail-reduced contrast images with a level of detail that is lower than in the microscope images and higher than in binary images.

Systems and methods for spatial analysis of analytes are provided. A data structure is obtained comprising an image, as an array of pixel values, of a sample on a substrate having a identifier, fiducial markers and a set of capture spots. The pixel values are used to identify derived fiducial spots. The substrate identifier identifies a template having reference positions for reference fiducial spots and a corresponding coordinate system. The derived fiducial spots are aligned with the reference fiducial spots using an alignment algorithm to obtain a transformation between the derived and reference fiducial spots. The transformation and the template corresponding coordinate system are used to register the image to the set of capture spots. The registered image is then analyzed in conjunction with spatial analyte data associated with each capture spot, thereby performing spatial analysis of analytes.

Aspects relate to reconstructing phase images from brightfield images at multiple focal planes using machine learning techniques. A machine learning model may be trained using a training data set comprised of matched sets of images, each matched set of images comprising a plurality of brightfield images at different focal planes and, optionally, a corresponding ground truth phase image. An initial training data set may include images selected based on image views of a specimen that are substantially free of undesired visual artifacts such as dust. The brightfield images of the training data set can then be modified based on simulating at least one visual artifact, generating an enhanced training data set for use in training the model. Output of the machine learning model may be compared to the ground truth phase images to train the model. The trained model may be used to generate phase images from input data sets.