| CPC G06T 7/143 (2017.01) [G06T 7/0012 (2013.01); G06T 7/12 (2017.01); G06T 7/149 (2017.01); G06V 10/70 (2022.01); G06V 20/695 (2022.01); G06T 2207/20016 (2013.01); G06T 2207/20116 (2013.01); G06T 2207/30024 (2013.01); G06V 10/52 (2022.01)] | 23 Claims |

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1. A method of segmenting one or more histological structures in a tissue image represented by multi-parameter cellular and sub-cellular imaging data, the method comprising:
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;
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.
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