Abstract: A method and system of operating a microscopy system are disclosed. A first image first image of a sample acquired using a first imaging configuration is received. A second image is developed from the first image, wherein the second image is associated with a second imaging configuration. A sequence of image processing steps is applied to the first image to develop a first classification of first objects represented in the first image and to the second image to develop a second classification of second objects represented in the second image. A score associated with the second imaging configuration is developed that represents a difference between the first classification and the second classification. The image acquisition time or component requirements of operating the microscopy system is less when operated using the second imaging configuration than when operated using the first 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 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.

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 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.

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 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.

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 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.