Segmenting a broad class of histological structures in transmitted light
and/or fluorescence-based images is a prerequisite for determining the
pathological basis of cancer, elucidating spatial interactions between
histological structures in tumor microenvironments (e.g. tumor infiltrating
lymphocytes), facilitating precision medicine studies with deep molecular
profiling, and providing an exploratory tool for pathologists. Our paper focuses
on segmenting histological structures in hematoxylin and eosin (H&E)
stained images of breast tissues, e.g. invasive carcinoma, carcinoma in situ,
atypical and normal ducts, adipose tissue, lymphocytes. We propose two
graph-theoretic segmentation methods based on local spatial color and nuclei
neighborhood statistics. For benchmarking, we curated a dataset of 232 high
power field breast tissue images together with expertly annotated ground truth.
To accurately model the preference for histological structures (ducts, vessels,
tumor nets, adipose etc.) over the remaining connective tissue and non-tissue
areas in ground truth annotations, we propose a new region-based score for
evaluating segmentation algorithms. We demonstrate the improvement of our
proposed methods over the state-of-the-art algorithms in both region and
boundary based performance measures.