SYSTEMS AND METHODS FOR AUTOMATIC GENERATION OF TRAINING SETS FOR MACHINE INTERPRETATION OF IMAGES
First Claim
Patent Images
1. A system, comprising:
- a processor;
a memory storing instructions configured to be executed by the processor, the instructions comprising instructions to;
receive image data;
generate an object identifier image from the image data;
extract a feature vector that contains intensity and shape information related to the object identifier image from the image data;
identify individual objects within the object identifier image;
associate a portion of the image with each individual object;
generate a feature vector for each individual object based on intensity values and shape descriptors associated with the portion of the image, such that the individual objects are associated with respective feature vectors;
identify a set of object groups within a distribution of the feature vectors;
identify a set of threshold values separating the object groups; and
assign a subset of individual objects to only one of the object groups using the set of threshold values and the feature vectors.
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Abstract
The subject matter of the present disclosure generally relates to techniques for image analysis. In certain embodiments, various morphological or intensity-based features as well as different thresholding approaches may be used to segment the subpopulation of interest and classify object in the images.
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Citations
22 Claims
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1. A system, comprising:
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a processor; a memory storing instructions configured to be executed by the processor, the instructions comprising instructions to; receive image data; generate an object identifier image from the image data; extract a feature vector that contains intensity and shape information related to the object identifier image from the image data; identify individual objects within the object identifier image; associate a portion of the image with each individual object; generate a feature vector for each individual object based on intensity values and shape descriptors associated with the portion of the image, such that the individual objects are associated with respective feature vectors; identify a set of object groups within a distribution of the feature vectors; identify a set of threshold values separating the object groups; and assign a subset of individual objects to only one of the object groups using the set of threshold values and the feature vectors. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 9)
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10. A method comprising:
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acquiring a set of images, wherein the set of images comprises a plurality of parameters; identifying individual objects within the set of images using the plurality of parameters; associating a subset of pixels with each individual object; integrating one or more intensity and shape feature vectors of the subset for each individual object in each of the one or more images to generate one or more multi-parametric feature vectors for each individual object; identifying a first group and a second group within a distribution of each of the one or more multi-parametric feature vectors; identifying a probability threshold separating the first group and the second group; assigning each individual object to only one of the plurality of groups using the one or more feature vectors and the probability threshold. - View Dependent Claims (11, 12, 13, 14, 15, 16)
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17. A method comprising:
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receiving a set of multiplexed images of a sample comprising a plurality of cells, wherein the set of multiplexed images comprises;
cell identifier image data of a cell identifier image, the cell identifier image data representative of binding of a cell identifier signal generator to a cell identifier target in the sample; and
cell marker image data of a cell marker image, the cell marker intensity data representative of binding of a cell marker signal generator to a cell marker target in the sample, wherein the cell marker image data comprises a cell marker intensity value for each pixel of the cell marker image;identifying individual cells within the sample using the cell identifier image data; associating a subset of pixels with each individual cell; integrating the marker intensity value across the pixels of the subset for each individual cell to generate an integrated marker intensity value for each individual cell; identifying a first group and a second group within a distribution of the integrated marker intensity values of the cells; identifying a threshold integrated marker intensity value separating the first group and the second group; and assigning each individual cell to only one of the plurality of groups using the integrated cell marker intensity value. - View Dependent Claims (18, 19, 20, 21, 22)
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Specification