A Two-Stage Approach for Segmenting Spatial Point Patterns Applied to Tumor Immunology

Oregon Convention Center 

In tumor immunology, clinical regimes corresponding to different stages of disease or responses to treatment may manifest as different spatial arrangements of tumor and immune cells. Spatial point pattern (SPP) modeling can be used to segment tissue images according to these regimes. To this end, we propose a two-stage approach: first, local intensities and pair correlation functions (PCF) are estimated from the SPP of cells within each image, and the PCFs are reduced in dimension via spectral decomposition of the covariance function. Second, the estimates are clustered in a Bayesian hierarchical model with spatially-dependent cluster labels. The clusters correspond to regimes of interest that are present across subjects; the cluster labels segment the images according to those regimes. Through Markov Chain Monte Carlo (MCMC) sampling, we jointly estimate and quantify uncertainty in the cluster assignment and spatial characteristics of each cluster. The number of clusters is found through cross-validation. Simulations demonstrate the performance of the method, and it is applied to a set of multiplex immunofluorescence images of pancreatic tissue.

Bayesian mixture model

Biomedical imaging

Functional data analysis

Tumor immunology

MCMC

Spatial point pattern 

Section on Statistics in Imaging