GMM Interpolation for Blood Cell Cluster Alignment in Childhood Leukaemia

Roxane Licandro, Konstantin Miloserdov, Michael Reiter, Martin Kampel

Abstract:

The accurate quantification of cancer (blast) and non-cancer cells in childhood leukaemia (blood cancer) is a key component in assessing the treatment response and to guide patient specific therapy. For this classification task, cell specific biomarker expression levels are estimated by using
flowcytometry measurements of multiple features of single blood cells. For the automated distinction between blasts and non-blasts a main challenge are data shifts and variations in the high-dimensional dataspace caused by instrumental drifts, interpatient variability, treatment response and different machine characteristics. In this work we present a novel alignment scheme for stable (non-cancer) cell populations in flowcytometry using Gaussian Mixture Models (GMM) as data representation format for the cell clusters’ probability density function and a Wasserstein interpolation scheme on the manifold of GMM. The evaluation is performed using a dataset of 116 patients with acute lymphoblastic leukaemia at treatment day 15. Classification results show an improved normalization performance using Wasserstein metric compared to two other metrics with a mean sensitivity of 0.97 and mean f-score of 0.95