Systems and methods for detection of structures and/or patterns in images
AssigneeVentana Medical Systems, Inc.
InventorsChristophe Chefd'hotel, Ting Chen
Life of this patent
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Abstract
The subject disclosure presents systems and computer-implemented methods for automatic immune cell detection that is of assistance in clinical immune profile studies. The automatic immune cell detection method involves retrieving a plurality of image channels from a multi-channel image such as an RGB image or biologically meaningful unmixed image. A cell detector is trained to identify the immune cells by a convolutional neural network in one or multiple image channels. Further, the automatic immune cell detection algorithm involves utilizing a non-maximum suppression algorithm to obtain the immune cell coordinates from a probability map of immune cell presence possibility generated from the convolutional neural network classifier.
Claims20
A computer-implemented method comprising:
- obtaining a training image of a biological specimen;
- separating, using color deconvolution, the training image into a plurality of color channels;
- obtaining a label for a biological structure in the biological specimen, wherein the label provides an identity and locator of the cellular structure, and the locator provides the location of the biological structure within the training image;
- extracting an image patch around the location of the biological structure from each color channel of the plurality of color channels;
- identifying the image patch and the label as a ground truth;
- and
- training a convolutional neural network model based on the ground truth.
A system comprising:
- one or more data processors;
- and
- a non-transitory computer readable storage medium containing instructions which, when executed on the one or more data processors, cause the one or more data processors to perform actions including:
- obtaining a training image of a biological specimen;
- separating, using color deconvolution, the training image into a plurality of color channels;
- obtaining a label for a biological structure in the biological specimen, wherein the label provides an identity and locator of the cellular structure, and the locator provides the location of the biological structure within the training image;
- extracting an image patch around the location of the biological structure from each color channel of the plurality of color channels;
- identifying the image patch and the label as a ground truth;
- and
- training a convolutional neural network model based on the ground truth.
A computer-program product tangibly embodied in a non-transitory machine-readable storage medium, including instructions configured to cause one or more data processors to perform actions including:
- obtaining a training image of a biological specimen;
- separating, using color deconvolution, the training image into a plurality of color channels;
- obtaining a label for a biological structure in the biological specimen, wherein the label provides an identity and locator of the cellular structure, and the locator provides the location of the biological structure within the training image;
- extracting an image patch around the location of the biological structure from each color channel of the plurality of color channels;
- identifying the image patch and the label as a ground truth;
- and
- training a convolutional neural network model based on the ground truth.
Dependencies are inferred from the claim text.