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

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.

A system of predicting 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 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.

A system of predicting 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 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.