Patents by Inventor Srinivas C. Chennubhotla

Srinivas C. Chennubhotla has filed for patents to protect the following inventions. This listing includes patent applications that are pending as well as patents that have already been granted by the United States Patent and Trademark Office (USPTO).

  • Patent number: 11983943
    Abstract: A computational systems pathology spatial analysis platform includes: (i) a spatial heterogeneity quantification component configured for generating a global quantification of spatial heterogeneity among cells of varying phenotypes in multi-parameter cellular and subcellular imaging data; (ii) a microdomain identification component configured for identifying a plurality of microdomains for tissue samples based on the global quantification, each microdomain being associated with a tissue sample; and (iii) a weighted graph component configured for constructing a weighted graph for the multi-parameter cellular and subcellular imaging data, the weighted graph having a plurality of nodes and a plurality of edges each being located between a pair of the nodes, wherein in the weighted graph each node is a particular one of the microdomains and the edge between each pair of microdomains in the weighted graph is indicative of a degree of similarity between the pair of the microdomains.
    Type: Grant
    Filed: December 16, 2019
    Date of Patent: May 14, 2024
    Assignee: UNIVERSITY OF PITTSBURGH-OF THE COMMONWEALTH SYSTEM OF HIGHER EDUCATION
    Inventors: Srinivas C. Chennubhotla, Filippo Pullara, Douglass L. Taylor
  • Patent number: 11972858
    Abstract: A method of characterizing cellular phenotypes includes receiving multi-parameter cellular and sub-cellular imaging data for a number of tissue samples from a number of patients or a number of multicellular in vitro models, performing cellular segmentation on the multi-parameter cellular and sub-cellular imaging data to create segmented multi-parameter cellular and sub-cellular imaging data, and performing recursive decomposition on the segmented multi-parameter cellular and subcellular imaging data to identify a plurality of computational phenotypes. The recursive decomposition includes a plurality of levels of decomposition with each level of decomposition including soft/probabilistic clustering and spatial regularization, and each cell in the segmented multi-parameter cellular and subcellular imaging data is probabilistically assigned to one or more of the plurality of computational phenotypes.
    Type: Grant
    Filed: May 13, 2020
    Date of Patent: April 30, 2024
    Assignee: University of Pittsburgh-Of the Commonwealth System of Higher Education
    Inventors: Srinivas C. Chennubhotla, Filippo Pullara, Samantha A. Furman
  • Publication number: 20240062564
    Abstract: A method of predicting cancer recurrence risk for an individual includes receiving patient spatial multi-parameter cellular and sub-cellular imaging data for a tumor of the individual, and analyzing the patient spatial multi-parameter cellular and sub-cellular imaging data using a prognostic model for predicting cancer recurrence risk to determine a predicted cancer recurrence risk for the individual, wherein the joint prognostic model is based on spatial correlation statistics among features derived for a plurality of intra-tumor spatial domains from spatial multi-parameter cellular and sub-cellular imaging data obtained from a plurality of cancer patients.
    Type: Application
    Filed: October 24, 2023
    Publication date: February 22, 2024
    Applicant: UNIVERSITY OF PITTSBURGH-OF THE COMMONWEALTH SYSTEM OF HIGHER EDUCATION
    Inventors: SRINIVAS C. CHENNUBHOTLA, DOUGLASS L. TAYLOR, SHIKHAR UTTAM FNU
  • Patent number: 11836998
    Abstract: A method of predicting cancer recurrence risk for an individual includes receiving patient spatial multi-parameter cellular and sub-cellular imaging data for a tumor of the individual, and analyzing the patient spatial multi-parameter cellular and sub-cellular imaging data using a prognostic model for predicting cancer recurrence risk to determine a predicted cancer recurrence risk for the individual, wherein the joint prognostic model is based on spatial correlation statistics among features derived for a plurality of intra-tumor spatial domains from spatial multi-parameter cellular and sub-cellular imaging data obtained from a plurality of cancer patients.
    Type: Grant
    Filed: May 23, 2019
    Date of Patent: December 5, 2023
    Assignee: University of Pittsburgh—Of the Commonwealth System of Higher Education
    Inventors: Srinivas C. Chennubhotla, Douglass L. Taylor, Shikhar Uttam Fnu
  • Publication number: 20230260256
    Abstract: A computational pathology method includes receiving multi-parameter cellular and/or sub-cellular imaging data for an image of a tissue sample, and locating and segmenting a plurality of tissue components of the tissue sample in the multi- parameter cellular and sub-cellular imaging data to generate segmented multi¬ parameter cellular and sub-cellular imaging data.
    Type: Application
    Filed: July 9, 2021
    Publication date: August 17, 2023
    Applicant: UNIVERSITY OF PITTSBURGH-OF THE COMMONWEALTH SYSTEM OF HIGHER EDUCATION
    Inventors: Srinivas C. Chennubhotla, Akif Burak Tosun, Jeffrey Fine
  • Publication number: 20230096719
    Abstract: A method (and system) of segmenting one or more histological structures in a tissue image represented by multi-parameter cellular and sub-cellular imaging data includes receiving coarsest level image data for the tissue image, wherein the coarsest level image data corresponds to a coarsest level of a multiscale representation of first data corresponding to the multi-parameter cellular and sub-cellular imaging data. The method further includes breaking the coarsest level image data into a plurality of non-overlapping superpixels, assigning each superpixel a probability of belonging to the one or more histological structures using a number of pre-trained machine learning algorithms to create a probability map, extracting an estimate of a boundary for the: one or more histological structures by applying a contour algorithm to the probability map, and using the estimate of the boundary to generate a refined boundary for the one or more histological structures.
    Type: Application
    Filed: March 16, 2021
    Publication date: March 30, 2023
    Applicant: University of Pittsburgh-Of the Commonwealth System of Higher Education
    Inventors: Srinivas C. Chennubhotla, Om Choudhary, Akif Burak Tosun, Jeffrey Fine
  • Publication number: 20220215935
    Abstract: A method of characterizing cellular phenotypes includes receiving multi-parameter cellular and sub-cellular imaging data for a number of tissue samples from a number of patients or a number of multicellular in vitro models, performing cellular segmentation on the multi-parameter cellular and sub-cellular imaging data to create segmented multi-parameter cellular and sub-cellular imaging data, and performing recursive decomposition on the segmented multi-parameter cellular and subcellular imaging data to identify a plurality of computational phenotypes. The recursive decomposition includes a plurality of levels of decomposition with each level of decomposition including soft/probabilistic clustering and spatial regularization, and each cell in the segmented multi-parameter cellular and subcellular imaging data is probabilistically assigned to one or more of the plurality of computational phenotypes.
    Type: Application
    Filed: May 13, 2020
    Publication date: July 7, 2022
    Applicant: University of Pittsburgh-Of the Commonwealth System of Higher Education
    Inventors: Srinivas C. Chennubhotla, Filippo Pullara, Samantha A. Furman
  • Publication number: 20220044401
    Abstract: A computational systems pathology spatial analysis platform includes: (i) a spatial heterogeneity quantification component configured for generating a global quantification of spatial heterogeneity among cells of varying phenotypes in multi-parameter cellular and subcellular imaging data; (ii) a microdomain identification component configured for identifying a plurality of microdomains for tissue samples based on the global quantification, each microdomain being associated with a a tissue sample; and (iii) a weighted graph component configured for constructing a weighted graph for the multi-parameter cellular and subcellular imaging data, the weighted graph having a plurality of nodes and a plurality of edges each being located between a pair of the nodes, wherein in the weighted graph each node is a particular one of the microdomains and the edge between each pair of microdomains in the weighted graph is indicative of a degree of similarity between the pair of the microdomains.
    Type: Application
    Filed: December 16, 2019
    Publication date: February 10, 2022
    Applicant: UNIVERSITY OF PITTSBURGH-OF THE COMMONWEALTH SYSTEM OF HIGHER EDUCATION
    Inventors: Srinivas C. Chennubhotla, Filippo Pullara, Douglass L. Taylor
  • Publication number: 20210383894
    Abstract: A method of generating a plurality of spatially co-registered data elements, each spatially co-registered data element being associated with and generated from a pair of co-registered tissue sections obtained from adjacent positions of a core taken from a tissue sample and including an image data section and a genomic data section. The method includes, for each pair of co-registered tissue sections: (i) obtaining and storing as part of a data element a plurality of multi to hyperplexed images from the imaging data section of the co-registered tissue section, (ii) generating and storing as part of the data element image data from the plurality of multi to hyperplexed images, and (iii) generating and storing as part of the data element genomic data from the genomic data section of the associated co-registered tissue section.
    Type: Application
    Filed: August 21, 2019
    Publication date: December 9, 2021
    Applicant: UNIVERSITY OF PITTSBURGH-OF THE COMMONWEALTH SYSTEM OF HIGHER EDUCATION
    Inventors: SRINIVAS C. CHENNUBHOTLA, ALBERT H. GOUGH, ANDREW M. STERN, MICHAEL J. BECICH, DOUGLASS L. TAYLOR
  • Publication number: 20210233659
    Abstract: A method of predicting cancer recurrence risk for an individual includes receiving patient spatial multi-parameter cellular and sub-cellular imaging data for a tumor of the individual, and analyzing the patient spatial multi-parameter cellular and sub-cellular imaging data using a prognostic model for predicting cancer recurrence risk to determine a predicted cancer recurrence risk for the individual, wherein the joint prognostic model is based on spatial correlation statistics among features derived for a plurality of intra-tumor spatial domains from spatial multi-parameter cellular and sub-cellular imaging data obtained from a plurality of cancer patients.
    Type: Application
    Filed: May 23, 2019
    Publication date: July 29, 2021
    Applicant: UNIVERSITY OF PITTSBURGH-OF THE COMMONWEALTH SYSTEM OF HIGHER EDUCATION
    Inventors: SRINIVAS C. CHENNUBHOTLA, DOUGLASS L. TAYLOR, SHIKHAR UTTAM FNU
  • Patent number: 9626583
    Abstract: In aspects, the subject innovation can comprise systems and methods capable of automatically labeling cell nuclei (e.g., epithelial nuclei) in tissue images containing multiple cell types. The enhancements to standard nuclei segmentation algorithms of the subject innovation can enable cell type specific analysis of nuclei, which has recently been shown to reveal novel disease biomarkers and improve diagnostic accuracy of computational disease classification models.
    Type: Grant
    Filed: December 12, 2014
    Date of Patent: April 18, 2017
    Assignee: University of Pittsburg—Of the Commonwealth System of Higher Education
    Inventors: Virginia M. Burger, Srinivas C. Chennubhotla
  • Publication number: 20150169985
    Abstract: In aspects, the subject innovation can comprise systems and methods capable of automatically labeling cell nuclei (e.g., epithelial nuclei) in tissue images containing multiple cell types. The enhancements to standard nuclei segmentation algorithms of the subject innovation can enable cell type specific analysis of nuclei, which has recently been shown to reveal novel disease biomarkers and improve diagnostic accuracy of computational disease classification models.
    Type: Application
    Filed: December 12, 2014
    Publication date: June 18, 2015
    Applicant: University of Pittsburgh - Of the Commonwealth System of Higher Education
    Inventors: Virginia M. Burger, Srinivas C. Chennubhotla