Molecular Devices Corporation

Moleculr Devices, LLC

Stephen Rhein

Avrum Isaac Cohen

Dihui Hong

Steve Luke

A high-content imaging system includes a stage, a controller, a machine learning system, and an image generator. The controller receives a request including an output imaging configuration and in response the controller: (1) selects a training model associated with the output imaging configuration, (2) determines an input imaging configuration associated with the training model, and (3) configures the high-content imaging system in accordance with the input imaging configuration. The machine learning system is configured using the training model so when the machine learning system is presented with an image acquired using the input imaging configuration, the machine learning system generates an output image in accordance with the output imaging configuration. The image generator generates an image of a sample on the stage and provides the generated image to the machine learning system and, in response, the machine learning system generates an output image in accordance with the output imaging configuration.

High-content imaging system to generate enhanced images and method of operating the same

A system and method for analyzing images using programmable device is disclosed. A sequencer operating on the non-transitory programmable device applies the first image analysis step to the first image to develop annotated training data. Specifications of the first image and the first image analysis step are developed using a graphical user interface operating on a computer. In addition, a machine learning system trainer operating on the programmable device trains an untrained machine learning system to develop a trained machine learning system. When the trained machine learning system is presented with the first image as an input, the trained machine learning system develops a prediction of the annotated training data. In addition, the sequencer analyzes a second image by undertaking a workflow, wherein the workflow is received from the computer and is specified using the graphical user interface and comprises a second image analysis step that that specifies operating the trained machine learning system.

Image analysis system and method of using the image analysis system

A computer implemented system and method for generating a focus corrected image of a sample disposed on a sample holder of an imaging system is disclosed. The imaging system includes an image sensor and a lens moveable relative to the image sensor between a first position and a second position. A characteristic map of the lens is developed that associates coordinates of each pixel of an image generated by the imaging sensor with one of a first plurality of locations of the lens between the first position and the second position. An image generator develops an output pixel of a focus-corrected image of a sample from a plurality of images of the sample acquired when the lens is positioned at a corresponding one of a second plurality of locations of the lens between the first position and the second position. The image generator selects a second location in accordance with the characteristic map, an image from the plurality of images of the sample associated with the second location, and determines a value of the output pixel in accordance with a value of a pixel of the selected one of the plurality of images that corresponds to the output pixel.

System and method to correct for variation of in-focus plane across a field of view of a microscope objective

A system and method for analyzing images using a non-transitory programmable device is disclosed. A user interface generator operating on the non-transitory programmable device receives specifications of a first image of a biological sample and a first image analysis step. A sequencer operating on the non-transitory programmable device applies the first image analysis step to the first image to develop annotated training data, and a machine learning system trainer operating on the non-transitory programmable device trains an untrained machine learning system to develop a trained machine learning system. When the trained machine learning system is presented with the first image as an input, the trained machine learning system develops a prediction of the annotated training data. In addition, the sequencer receives a specification of a workflow that comprises a second image analysis step that specifies operating the trained machine learning system and the sequencer undertakes the workflow on a second image to analyze the second image.

A system and method of analyzing a biological sample using an imaging system are disclosed. An image acquisition module instructs the imaging system to obtain a label free image of a training biological sample and in response receives a first training image. The image acquisition module also instructs the imaging system to cause the training biological sample to fluoresce and obtain an image of the training biological sample undergoing fluorescence, and in response receives a second training image. An analysis module analyzing the second training image to generate a plurality of training cell characteristics, wherein each of the plurality training cell characteristics is associated with one of a plurality of training cells that comprise the training biological sample. A training module trains a machine learning system using the first training image and the plurality of training cell characteristics to develop a trained machine learning system such that when the trained machine learning system is operated with the first training image as an input, the trained machine learning system generates a plurality of predicted cell characteristics that correspond to the plurality of training cell characteristics.

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Phoenixville, PA, United States of America

Stephen Rhein