Abstract Number:
3238
Submission Type:
Contributed Abstract
Contributed Abstract Type:
Paper
Participants:
Alvin Sheng (1), Brian Reich (1), Ana-Maria Staicu (1)
Institutions:
(1) North Carolina State University, N/A
Co-Author(s):
First Author:
Presenting Author:
Abstract Text:
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) statistics can be used to partition tissue images according to these regimes. We propose a two-stage approach: first, local intensities and pair correlation functions (PCF) are estimated from the SPP of cells within each image. PCFs are reduced in dimension via spectral decomposition of the covariance function. Second, the estimates are clustered in a Bayesian mixture 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 sampling, we jointly estimate and quantify uncertainty in the cluster assignment probabilities, the spatial dependence parameter, and the 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.
Keywords:
Bayesian mixture model|Biomedical imaging|Functional data analysis|Tumor immunology|MCMC|Spatial point pattern
Sponsors:
Section on Statistics in Imaging
Tracks:
Miscellaneous
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