Topological segmentation of mass spectrometry imaging data.

ABSTRACT:

Introduction

Image segmentation is an important challenge in mass spectrometry imaging data processing. Here, we report an unsupervised topological segmentation method adapted to the specific nature of mass spectrometry data. Unlike machine learning clustering algorithms, the proposed method retains the physical and chemical integrity of the mass spectrum, as no dimensionality reduction is required.

Methods

Using the cosine similarity measure, we discard outliers, detect spectrally homogeneous regions, and filter pixels with mixed cell origin on the border of different tissue subtypes. Then, we evaluate the actual data manifold dimensionality to determine spectrally homogeneous regions within samples. The method was implemented to discriminate regions related to sections of

SUBMITTER: Derkach MM 

PROVIDER: S-EPMC12553024 | biostudies-literature | 2025 Dec

REPOSITORIES: biostudies-literature