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Activity Number: 488 - Novel Methods for Unique Spatial Imaging Applications
Type: Contributed
Date/Time: Wednesday, August 10, 2022 : 2:00 PM to 3:50 PM
Sponsor: Section on Statistics in Imaging
Abstract #323272
Title: Multilayer Adjusted Cluster Point Process Model: Application to Microbial Biofilm Image Data Analysis
Author(s): Suman Majumder* and Brent Coull and Jessica L. Mark Welch and Patrick J. La Riviere and Floyd E. Dewhirst and Jacqeline R. Starr and Kyu Ha Lee
Companies: Harvard T.H. Chan School of Public Health and Harvard University and Marine Biological Laboratory and University of Chicago and The Forsyth Institute and Brigham and Women's Hospital and Harvard T.H. Chan School of Public Health
Keywords: Dental plaque sample; Imaging; Parent-offspring models; Point processes; Spatial statistics; Thomas processes

Clusters of similar or dissimilar objects are seen in many fields, like ecology, astronomy and biomedical imaging. Still challenging is to quantify spatial clustering when one or more entities cluster around a different entity in multiple layers. Such structures are observed in human dental plaque biofilm images, which exhibit multi-taxa structures in corncob-like arrangements. We propose a novel multivariate spatial point process model to quantify corncob-like arrangements with parent-offspring models. The proposed multilayer adjusted cluster point process (MACPP) model departs from common methods in exploiting locations of parent objects in clusters and accounts for multilayered, multivariate parent-offspring clustering. In simulated data, MACPP outperforms classical univariate Neyman-Scott process model for modeling spatially clustered processes, by producing decisively more accurate and precise parameter estimates. We analyzed data from a human dental plaque biofilm image in which Streptococcus and Porphyomonas simultaneously cluster around Corynebacterium, and Pasteurellaceae around Streptococcus. MACPP successfully captured the parent-offspring structure for all taxa involved

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