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Systems and methods for segmentation and processing of tissue images and feature extraction from same for treating, diagnosing, or predicting medical conditions

  • US 9,971,931 B2
  • Filed: 07/26/2017
  • Issued: 05/15/2018
  • Est. Priority Date: 07/30/2010
  • Status: Active Grant
First Claim
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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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