Detecting latent spatial patterns in mass spectrometry brain imaging data via Bayesian mixtures

G. Capitoli, S. Colombara, A. Cotroneo, F. De Caro, R. Morandi, C. Schembri, A. G. Zapiola, Francesco Denti

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Mass spectrometry methods can record biomolecule abundance for a broad set of molec- ular masses given a sample of a specific biological tissue. In particular, the MALDI-MSI technique produces imaging data where, for each pixel, a mass spectrum is recorded. There is the urge to rely on suited statistical methods to model these data, fully addressing their morphological characteristics. Here, we investigate the use of Bayesian mixture models to segment these real biomedical images. We aim to detect groups of pixels that present sim- ilar patterns to extract interesting insights, such as anomalies that one cannot capture from the original pictures. This task is particularly challenging given the high dimensionality of the data and the spatial correlation among pixels. To account for the spatial nature of the dataset, we rely on Hidden Markov Random Fields.
Original languageEnglish
Title of host publicationBook of the Short Papers SEAS IN 2023
Pages1127-1132
Number of pages6
Publication statusPublished - 2023
EventSIS 2023 - Statistical Learning, Sustainability and Impact Evaluation - Ancona
Duration: 21 Jun 202323 Jun 2023

Conference

ConferenceSIS 2023 - Statistical Learning, Sustainability and Impact Evaluation
CityAncona
Period21/6/2323/6/23

Keywords

  • Mass spectrometry
  • Brain imaging
  • Potts model
  • Bayesian mixture models

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