University of Pittsburgh

Om Choudhary

Srinivas C Chennubhotla

Akif Burak Tosun

Jeffrey Fine

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.

Scalable and high precision context-guided segmentation of histological structures including ducts/glands and lumen, cluster of ducts/glands, and individual nuclei in whole slide images of tissue samples from spatial multi-parameter cellular and sub-cellular imaging platforms

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Pittsburgh, PA, United States of America

Om Choudhary