Molecular Devices Corporation

Dh Technologies Development Pte. Ltd.

Moleculr Devices, LLC

Dihui Hong

Avrum Isaac Cohen

Stephen Rhein

Steven Luke

Steve Luke

Ting Zhao

Avrum I Cohen

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 computer-implemented system and method for analyzing a biological sample are disclosed. A sequence of images of the biological sample from an image capture device are received from an image capture device, wherein each image of the sequence of images is acquired at a particular focal plane of the biological sample. In addition, an object map is developed from the sequence of images and the object map is analyzed to measure a three-dimensional characteristic of a particular object. The object map comprises a plurality of object map planes, each object map plane is associated with one image of the sequence of images, and pixels of the object map planes associated with the particular object in the sequence of images are assigned a unique identifier associated with the particular object.

System and method for image analysis of multi-dimensional data

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 a method to analyze a phenotypical response of cells to a treatment are disclosed in which a model development module receives images of a plurality of reference cell carriers and treatment information associated with the plurality of reference cell carriers, identifies parameters of cells in the image that distinguish those reference cell carriers to which the treatment has been applied from other reference cell carriers, and trains a model using the identified parameters. A high-content imaging system includes an image capture device, and the image acquisition module receives from the image capture device a plurality of images of cell carriers to be evaluated. The model application module applies the trained model to the plurality of images of the cell carriers to be evaluated to predict a concentration of the treatment applied to each of the cell carriers evaluated.

System and method for automatically analyzing phenotypical responses of cells

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.

System and method for label-free identification and classification of biological samples

A system and method for analyzing multi-dimensional images includes a high content imaging system that includes an image capture device. An image acquisition module receives a series of images of a biological sample captured by the image capture device, and the series of images includes a sequence of image planes. A human interface module receives from a user computer specifications of a first image analysis step and a second image analysis step. The first image analysis step specifies a first image processing operation that processes an image plane of a series of images in accordance with at least another image plane of the series of images and the second image analysis step specifies a second image processing operation that processes each image plane of a series of images independently of the other image planes of the series. An image analysis module having a plurality of processors operating in parallel processes the first series of images in accordance with the first image processing step to generate a first output series of images, and processes the first series of images in accordance with second image processing step to generate a second output series of images. The human interface module displays at least one image plane of the first output series of images and at least one image plane of the second output series of images on a display associated with the user computer.

A system and method to segment an image captured from an image capture device of a high content imaging system includes an image acquisition module that receives the image captured by the image capture device. A coarse object detection module develops a coarse segmented image, wherein each pixel of the coarse segmented image is associated with a corresponding pixel in the captured image and is identified as one of an object pixel and a background pixel. A marker identification module selects at least one marker pixel from the pixels of the coarse segmented image, wherein each marker pixel is one of a contiguous group of object pixels in the coarse segmented image that is furthest from a background pixel relative to neighboring pixels of the group. An object splitting module that comprises a plurality of processors operating in parallel that associates each object pixel of the coarse segmented image with a marker pixel, wherein a distance based metric between the object pixel and the marker pixel is less than the distance based metric between the object pixel and any other marker pixel in the coarse segmented image.

System and method for segmentation of three-dimensional microscope images

In a method and system for identifying objects in an image, an image and training data are received. The training data identifies a pixel associated with an object of a particular type in the image. A plurality of filtered versions of the image are developed. The training data and the plurality of filtered versions of the image are processed to develop a trained model for classifying pixels associated with objects of the particular type. The trained model is applied to the image to identify pixels associated a plurality of objects of the particular type in the image. Additional image processing steps are developed to further refine the identified pixels for better fitting of the contour of the objects with their edges.

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Chester Springs, PA, United States of America

Dihui Hong