University of Pittsburgh

S Chakra Chennubhotla

D Lansing Taylor

Graph-theoretic segmentation methods for segmenting histological structures in H&E stained images of tissues. The method rely on characterizing local spatial statistics in the images. Also, a method for quantifying intratumor spatial heterogeneity that can work with single biomarker, multiplexed, or hyperplexed immunofluorescence (IF) data. The method is holistic in its approach, using both the expression and spatial information of an entire tumor tissue section and/or spot in a TMA to characterize spatial associations. The method generates a two-dimensional heterogeneity map to explicitly elucidate spatial associations of both major and minor sub-populations.

Systems and methods for finding regions of in interest in hematoxylin and eosin (HandE) stained tissue images and quantifying intratumor cellular spatial heterogeneity in multiplexed/hyperplexed fluorescence tissue

Graph-theoretic segmentation methods for segmenting histological structures in H&E stained images of tissues. The method relies on characterizing local spatial statistics in the images. Also, a method for quantifying intratumor spatial heterogeneity that can work with single biomarker, multiplexed, or hyperplexed immunofluorescence (IF) data. The method is holistic in its approach, using both the expression and spatial information of an entire tumor tissue section and/or spot in a TMA to characterize spatial associations. The method generates a two-dimensional heterogeneity map to explicitly elucidate spatial associations of both major and minor sub-populations.

Systems and methods for finding regions of interest in hematoxylin and eosin (H and E) stained tissue images and quantifying intratumor cellular spatial heterogeneity in multiplexed/hyperplexed fluorescence tissue images

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Pittsburgh, PA, United States of America

S Chakra Chennubhotla