  Vascular calcification is implicated as an important factor in major adverse
cardiovascular events (MACE), including heart attack and stroke. A controversy
remains over how to integrate the diverse forms of vascular calcification into
clinical risk assessment tools. Even the commonly used calcium score for
coronary arteries, which assumes risk scales positively with total
calcification, has important inconsistencies. Fundamental studies are needed to
determine how risk is influenced by the diverse calcification phenotypes.
However, studies of these kinds are hindered by the lack of high-throughput,
objective, and non-destructive tools for classifying calcification in imaging
data sets. Here, we introduce a new classification system for phenotyping
calcification along with a semi-automated, non-destructive pipeline that can
distinguish these phenotypes in even atherosclerotic tissues. The pipeline
includes a deep-learning-based framework for segmenting lipid pools in noisy
micro-CT images and an unsupervised clustering framework for categorizing
calcification based on size, clustering, and topology. This approach is
illustrated for five vascular specimens, providing phenotyping for thousands of
calcification particles across as many as 3200 images in less than seven hours.
Average Dice Similarity Coefficients of 0.96 and 0.87 could be achieved for
tissue and lipid pool, respectively, with training and validation needed on
only 13 images despite the high heterogeneity in these tissues. By introducing
an efficient and comprehensive approach to phenotyping calcification, this work
enables large-scale studies to identify a more reliable indicator of the risk
of cardiovascular events, a leading cause of global mortality and morbidity.
