Systems and methods for automatic detection of architectural distortion in two dimensional mammographic images
First Claim
1. A method for using a trained statistical classifier for detecting an indication of architectural distortion in a mammographic image, comprising:
- receiving a two dimensional (2D) mammographic image of a breast;
segmenting fibroglandular tissue of the breast to create a segmented fibroglandular tissue region;
extracting a plurality of regions within the segmented fibroglandular tissue region and within a boundary portion between the segmented fibroglandular tissue and non-fibroglandular tissue;
computing representations for each region of interest (RoI) by a pre-trained deep neural network;
training a classifier on the computed representations to compute a respective probability score of architectural distortion associated with each RoI;
defining each RoI having the probability score above a threshold as positive for architectural distortion;
clustering the RoIs defined as positive using a mean-shift method and providing an indication of the probability of the presence of architectural distortion around a cluster based on a probability distribution of cluster RoI members;
removing small clusters created by the clustering of the RoI according to a small number threshold, wherein clusters having fewer RoI members than the small number threshold are removed;
classifying the image as positive for the indication of architectural distortion when at least one cluster remains after the removing, or classifying the image as negative for the indication of architectural distortion when no cluster remains after the removing; and
outputting a classification of the image.
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Accused Products
Abstract
There is provided a method, comprising: segmenting fibroglandular tissue of a 2D mammographic image of a breast, extracting regions within the segmented fibroglandular tissue and within a boundary portion between the segmented fibroglandular tissue and non-fibroglandular tissue, computing representations for each RoI by a pre-trained deep neural network, training a classifier on the representations to compute a probability score of architectural distortion for each RoI, clustering RoIs defined as positive for architectural distortion using a mean-shift method and providing an indication of the probability of the presence of architectural distortion around a cluster based on the probability distribution of cluster RoI members, removing small clusters having fewer RoI members than a small number threshold, classifying the image as positive for the indication of architectural distortion when at least one cluster remains, or classifying the image as negative for the indication of architectural distortion when no cluster remains.
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Citations
20 Claims
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1. A method for using a trained statistical classifier for detecting an indication of architectural distortion in a mammographic image, comprising:
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receiving a two dimensional (2D) mammographic image of a breast; segmenting fibroglandular tissue of the breast to create a segmented fibroglandular tissue region; extracting a plurality of regions within the segmented fibroglandular tissue region and within a boundary portion between the segmented fibroglandular tissue and non-fibroglandular tissue; computing representations for each region of interest (RoI) by a pre-trained deep neural network; training a classifier on the computed representations to compute a respective probability score of architectural distortion associated with each RoI; defining each RoI having the probability score above a threshold as positive for architectural distortion; clustering the RoIs defined as positive using a mean-shift method and providing an indication of the probability of the presence of architectural distortion around a cluster based on a probability distribution of cluster RoI members; removing small clusters created by the clustering of the RoI according to a small number threshold, wherein clusters having fewer RoI members than the small number threshold are removed; classifying the image as positive for the indication of architectural distortion when at least one cluster remains after the removing, or classifying the image as negative for the indication of architectural distortion when no cluster remains after the removing; and outputting a classification of the image. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13)
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14. A method for training a statistical classifier for detecting an indication of architectural distortion in a mammographic image of a breast, comprising:
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receiving a set of training 2D mammographic member images including a sub-set of 2D mammographic images labeled as positive for architectural distortion, wherein the 2D mammographic images comprise screening mammographic images, wherein a size of the sub-set being inadequate for training a standard R-CNN to achieve statistically significant classification; segmenting fibroglandular tissue of the breast of each member image to create a segmented fibroglandular tissue region; extracting positive RoIs from regions around an identified architectural distortion of the sub-set of 2D mammographic images labeled as positive for architectural distortion; extracting negative RoIs from random regions in the fibroglandular tissues of normal mammographic images that are not labeled as positive for architectural distortion;
computing representations for each RoI using a pre-trained neural network;training a binary object cascade classifier, using the computed representations, to compute a respective probability score indicative of architectural distortion associated with each RoI; and providing the trained binary object cascade classifier for classifying a new 2D mammographic image as positive for the indication of architectural distortion. - View Dependent Claims (15, 16, 17, 18, 19)
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20. A system for using a trained statistical classifier for detecting an indication of architectural distortion in a mammographic image, comprising:
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a program store storing code; and a processor coupled to the program store for implementing the stored code, the code comprising; code to receive a two dimensional (2D) mammographic image of a breast; code to segment fibroglandular tissue of the breast to create a segmented fibroglandular tissue region, extract a plurality of regions within the segmented fibroglandular tissue region and within a boundary portion between the segmented fibroglandular tissue and non-fibroglandular tissue, compute representations for each RoI by a neural network trained on a plurality of sample 2D mammographic images using automatically identified and extracted features, apply a classifier to the computed representations to compute a respective probability score of architectural distortion associated with each RoI, define each RoI having the probability score above a threshold as positive for architectural distortion, cluster the RoIs defined as positive using a mean-shift method to provide an indication of the probability of the presence of architectural distortion for each respective RoI, remove small clusters created by the clustering of the RoI according to a small number threshold, wherein clusters having fewer RoI members than the small number threshold are removed, classify the image as positive for the indication of architectural distortion when at least one cluster remains after the removing, or classify the image as negative for the indication of architectural distortion when no cluster remains after the removing; and output a classification of the image.
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Specification