Systems and methods for segmentation and processing of tissue images and feature extraction from same for treating, diagnosing, or predicting medical conditions
First Claim
1. A system for predicting the occurrence of a medical condition, the system comprising:
- (1) a database configured to store patient data, including at least one sample image of a tissue sample treated with a plurality of flurochrome labeled antibodies; and
(2) a processor configured by code executing therein to perform the following;
(a) generate a patient image dataset, using the at least one sample image, that includes values for one or more texture features selected from a group of features consisting of (i) homogeneity and (ii) correlation;
(b) evaluate at least the patient image dataset with a Support Vector Regression for Censored Data (SVRc) algorithm executed as code by the processor, where the SVRc algorithm is configured to output a value corresponding to a risk score for a medical condition occurrence based on the patient image dataset, wherein the SVRc algorithm is generated by performing regression, using code executed in the processor, on a population dataset, where each member of the population has measurement values corresponding to each feature of the patient image dataset;
(c) assign the patient to a high probability of a medical condition occurrence where the output value is below a pre-determined threshold and assign the patient to a low probability of a medical condition occurrence where the output value is above the pre-determined threshold; and
(d) generate a report based on the updated patient dataset.
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Abstract
Apparatus, methods, and computer-readable media are provided for segmentation, processing (e.g., preprocessing and/or postprocessing), and/or feature extraction from tissue images such as, for example, images of nuclei and/or cytoplasm. Tissue images processed by various embodiments described herein may be generated by Hematoxylin and Eosin (H&E) staining, immunofluorescence (IF) detection, immunohistochemistry (IHC), similar and/or related staining processes, and/or other processes. Predictive features described herein may be provided for use in, for example, one or more predictive models for treating, diagnosing, and/or predicting the occurrence (e.g., recurrence) of one or more medical conditions such as, for example, cancer or other types of disease.
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Citations
17 Claims
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1. A system for predicting the occurrence of a medical condition, the system comprising:
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(1) a database configured to store patient data, including at least one sample image of a tissue sample treated with a plurality of flurochrome labeled antibodies; and (2) a processor configured by code executing therein to perform the following; (a) generate a patient image dataset, using the at least one sample image, that includes values for one or more texture features selected from a group of features consisting of (i) homogeneity and (ii) correlation; (b) evaluate at least the patient image dataset with a Support Vector Regression for Censored Data (SVRc) algorithm executed as code by the processor, where the SVRc algorithm is configured to output a value corresponding to a risk score for a medical condition occurrence based on the patient image dataset, wherein the SVRc algorithm is generated by performing regression, using code executed in the processor, on a population dataset, where each member of the population has measurement values corresponding to each feature of the patient image dataset; (c) assign the patient to a high probability of a medical condition occurrence where the output value is below a pre-determined threshold and assign the patient to a low probability of a medical condition occurrence where the output value is above the pre-determined threshold; and (d) generate a report based on the updated patient dataset. - View Dependent Claims (2, 3, 4)
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5. A system of predicting occurrence of a medical condition, the system comprising:
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(1) a database configured to store patient data, including at least one sample image of a tissue sample treated with a plurality of flurochrome labeled antibodies; and (2) a processor configured by code executing therein to perform the following; (a) generate a patient morphometric dataset, using the at least one sample image, that includes values for at least one of the following features (1) one or more values selected from a group of ring measurements of one or more ring metrics; and
(2) one or more values derived from one or more ring metrics, wherein the one or more values represents an adjacency relationship between rings;(b) evaluate at least the patient morphometric dataset with a Support Vector Regression for Censored Data (SVRc) algorithm executed as code by the processor, where the SVRc algorithm is configured to output a value corresponding to a risk score for a medical condition occurrence based on the morphometric dataset, wherein the SVRc algorithm is generated by performing regression, using code executed in the processor, on a population dataset, where each member of the population has measurement values corresponding to each feature of the patient morphometric dataset; (c) assign the patient to a high probability of a medical condition occurrence where the output model value is below a pre-determined threshold and assign the patient to a low probability of a medical condition occurrence where the output value is above the pre-determined threshold; and (d) generate a report based on the updated patient dataset. - View Dependent Claims (6, 7, 8, 9, 10, 11, 12, 13, 14, 15)
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16. A system of predicting occurrence of a medical condition, the system comprising:
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(1) a database configured to store patient data, including at least one sample image of a tissue sample treated with a plurality of flurochrome labeled antibodies; and (2) a processor configured by code executing therein to perform the following; (a) generate a patient image dataset, using the at least one sample image, that includes values for at least one of the following features;
(1) a feature generated based upon a comparison of histograms, said histograms corresponding to compartments or sub-compartments of cellular objects and (2) a feature generated from an intensity index corresponding to image signal intensity;(b) evaluate at least the patient image dataset with a Support Vector Regression for Censored Data (SVRc) algorithm executed as code by the processor, where the SVRc algorithm is configured to output a value corresponding to a risk score for a medical condition occurrence based on the morphometric dataset, wherein the SVRc algorithm is generated by performing regression, using code executed in the processor, on a population dataset, where each member of the population has measurement values corresponding to each feature of the patient image dataset; (c) assign the patient to a high probability of a medical condition occurrence where the output model value is below a pre-determined threshold and assign the patient to a low probability of a medical condition occurrence where the output value is above the pre-determined threshold; and (d) generate a report based on the updated patient dataset. - View Dependent Claims (17)
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Specification