ArticleBioinformatics (Oxford, England)2011
Efficient spatial segmentation of large imaging mass spectrometry datasets with spatially aware clustering.
Article in Bioinformatics (Oxford, England), 2011. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 60 papers.
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60 citing papers in PubMed.
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- Spatial transcriptomic-metabolic features of tumor foci and tumor capsule in microvascular invasion with hepatocellular carcinoma: A spatial multi-omics study.PLoS medicine · 2026Article
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- Mass spectrometry imaging tutorial: From cancer biomarker discovery to clinical applications.Analytica chimica acta · 2026Review
- spatialstein: An Open-Source Workflow for Annotation, Deconvolution, and Spatially Aware Segmentation of Mass Spectrometry Imaging Data.Analytical chemistry · 2026Article
- Dimensional cophenetic integrity: a method for evaluation of dimensionality reduction in MSI.Bioinformatics advances · 2026Article
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- Unstained Blood Smear Analysis: A Review of Rule-Based, Machine Learning, and Deep Learning Techniques.Journal of biophotonics · 2025Review
- Computational method for mapping mass signatures along developmental gradients reveals a novel role for a monosaccharide tetrose in maize salt-stress response.bioRxiv : the preprint server for biology · 2025Article
- Efficient Compression of Mass Spectrometry Images via Contrastive Learning-Based Encoding.Analytical chemistry · 2025Article
- Mass-Guided Single-Cell MALDI Imaging of Low-Mass Metabolites Reveals Cellular Activation Markers.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025Article
- msiFlow: automated workflows for reproducible and scalable multimodal mass spectrometry imaging and microscopy data analysis.Nature communications · 2025Article
- Matrix-assisted laser desorption/ionization mass spectrometry imaging as a new tool for molecular histopathology in epilepsy surgery.Epilepsia · 2024Article
- Consensus tissue domain detection in spatial omics data using multiplex image labeling with regional morphology (MILWRM).Communications biology · 2024Article
- Extended similarity methods for efficient data mining in imaging mass spectrometry.Digital discovery · 2024Article
- NECTAR: A New Algorithm for Characterizing and Correcting Noise in QToF-Mass Spectrometry Imaging Data.Journal of the American Society for Mass Spectrometry · 2023Article
- How the Oviduct Lipidomic Profile Changes over Time after the Start of an Obesogenic Diet in an Outbred Mouse Model.Biology · 2023Article
Corrections and comments
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2 authors.
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Abstract
motivationImaging mass spectrometry (IMS) is one of the few measurement technology s of biochemistry which, given a thin sample, is able to reveal its spatial chemical composition in the full molecular range. IMS produces a hyperspectral image, where for each pixel a high-dimensional mass spectrum is measured. Currently, the technology is mature enough and one of the major problems preventing its spreading is the under-development of computational methods for mining huge IMS datasets. This article proposes a novel approach for spatial segmentation of an IMS dataset, which is constructed considering the important issue of pixel-to-pixel variability.
methodsWe segment pixels by clustering their mass spectra. Importantly, we incorporate spatial relations between pixels into clustering, so that pixels are clustered together with their neighbors. We propose two methods. One is non-adaptive, where pixel neighborhoods are selected in the same manner for all pixels. The second one respects the structure observable in the data. For a pixel, its neighborhood is defined taking into account similarity of its spectrum to the spectra of adjacent pixels. Both methods have the linear complexity and require linear memory space (in the number of spectra).
resultsThe proposed segmentation methods are evaluated on two IMS datasets: a rat brain section and a section of a neuroendocrine tumor. They discover anatomical structure, discriminate the tumor region and highlight functionally similar regions. Moreover, our methods provide segmentation maps of similar or better quality if compared to the other state-of-the-art methods, but outperform them in runtime and/or required memory. CONTACT: theodore@math.uni-bremen.de.
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