Perkinelmer Cellular Technologies Germany Gmbh

Evotec Ag

Evotec Oai Ag

Perkinelmer Health Sciences Canada, Inc.

Revvity Cellular Technologies Gmbh

Kaupo Palo

Peet Kask

Hartwig Preckel

S Chris Hinnah

Thomas Steinkamp

Karsten Gall

Olavi Ollikainen

Stefan Jäger

Stefan Lüpke

Nicolas Fay

Maciej Hoffman-Wecker

Ollavi Ollikainen

Pierre Ilouga

Ülo Mets

Christian Eggeling

Abdulrahman Alhaimi

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.

Reconstructing phase images with deep learning

Certain configurations are described of methods and systems that can be used to image three-dimensional objects such as biological cells, biological tissues or biological organisms. The methods and systems can image the three-dimensional objects at reduced imaging times and with reduced data volumes.

Methods and systems for automated imaging of three-dimensional objects

Described herein are computationally efficient systems and methods for processing and analyzing two-dimensional (2D) and three-dimensional (3D) images using texture filters that are based on the Hessian eigenvalues (e.g., eigenvalues of a square matrix of second-order partial derivatives) of each pixel or voxel. The original image may be a single image or a set of multiple images. In certain embodiments, the filtered images are used to calculate texture feature values for objects such as cells identified in the image. Once objects are identified, the filtered images can be used to classify the objects, for image segmentation, and/or to quantify the objects (e.g., via regression).

Systems and methods for segmentation and analysis of 3D images

Described herein is a method for adjusting one or more images of a sample to correct geometric distortions and/or to properly align the one or more images using a pattern of dots, e.g., a quasiperiodic grid.

Method and system for image correction using a quasiperiodic grid

A method for identifying the impact on data, such as experimental data, of interfering effects, such as unwanted auto-fluorescence, fluorescence quenching, and fluorescent-sample deterioration, whether or not the data fulfill certain criteria with respect to a threshold indicative of the interfering effects.

Method for detecting the impacts of interfering effects on experimental data

An apparatus for confocal observation of a specimen includes an illumination device. The illumination device generates illumination radiations of at least two different wavelengths. With the aid of a mask device illuminated by the illumination radiations, one mask image is generated per wavelength. An objective serves for imaging said mask images in the specimen. With the aid of a beam splitter device, the emission radiations emitted by the specimen are divided based on the wavelengths, and are detected based on the wavelengths by a detection device.

Apparatus for confocal observation of a specimen

A method for distinguishing samples having flourescent particles includes (a) monitoring intensity fluctuations of fluorescence emitted by the particles in at least one measurement volume by detecting sequences of photon counts by at least one photon detector, (b) determining, from the sequences of photon counts, intermediate statistical data involving at least two probability functions of the number of photon counts detected in different sets of counting time intervals, and (c) determining from the intermediate statistical data a distribution of particles as a function of at least two arguments, wherein one argument is a specific brightness of the particles, or a measure thereof, and another argument is a diffusion coefficient of the particles, or a measure thereof.

Fluorescence intensity multiple distributions analysis: concurrent determination of diffusion times and molecular brightness

A method for characterizing fluorescent molecules or other particles in samples comprising the steps of:

Method of characterizing fluorescent molecules or other particles using generating functions

A method characterizes fluorescent molecules or other particles in samples by a) monitoring fluctuating intensity of fluorescence emitted by the molecules or other particles in at least one measurement volume of a non-uniform spatial brightness profile by measuring numbers of photon counts in primary time intervals by a single or more photon detectors, b) determining at least one distribution function of numbers of photon counts, {circumflex over (P)}(n), from the measured numbers of photon counts, c) determining physical quantities characteristic to the particles by fitting the experimentally determined distribution function of numbers of photon counts, wherein the fitting procedure involves calculation of a theoretical distribution function of the number of photon counts P(n) through its generating function, defined as

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Kaupo Palo