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

摘要

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.