Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.