Correlation Clustering of Organoid Images
Jannik Presberger Rashmiparvathi Keshara David Stein Yung Hae Kim Anne Grapin-Botton Bjoern Andres
In biological and medical research, scientists now routinely acquire
microscopy images of hundreds of morphologically heterogeneous organoids and
are then faced with the task of finding patterns in the image collection, i.e.,
subsets of organoids that appear similar and potentially represent the same
morphological class. We adopt models and algorithms for correlating organoid
images, i.e., for quantifying the similarity in appearance and geometry of the
organoids they depict, and for clustering organoid images by consolidating
conflicting correlations. For correlating organoid images, we adopt and compare
two alternatives, a partial quadratic assignment problem and a twin network.
For clustering organoid images, we employ the correlation clustering problem.
Empirically, we learn the parameters of these models, infer a clustering of
organoid images, and quantify the accuracy of the inferred clusters, with
respect to a training set and a test set we contribute of state-of-the-art
light microscopy images of organoids clustered manually by biologists.