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Related Concept Videos

MALDI-TOF Mass Spectrometry01:19

MALDI-TOF Mass Spectrometry

Mass spectrometry is a powerful characterization technique that can identify and separate a wide variety of compounds ranging from chemical to biological entities, based on their mass-to-charge ratio (m/z). The instruments that allow this detection, known as mass spectrometers, have three components: an ion source, a mass analyzer, and a detector. These spectrometers differ based on the nature of their ion source and analyzers.Matrix-assisted laser desorption ionization (MALDI) is a commonly...

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Segmentation and Multimodal Characterization of Metal Particles in the Human Hippocampus Using Discrete Segmentation

Ján Pánik1, Mária Ždímalová2, Daniel Kosnáč1

  • 1Institute of Medical Physics and Biophysics, Faculty of Medicine, Comenius University, Sasinkova 2, 813 72 Bratislava, Slovakia.

Molecules (Basel, Switzerland)
|January 10, 2026
PubMed
Summary
This summary is machine-generated.

Researchers developed a new method to segment and analyze metallic nanoparticles in the brain. This technique helps understand their link to neurodegenerative diseases by detailing particle composition and shape.

Keywords:
biomedical imaginghuman hippocampusmaximum flowmetallic particlesspectral analysis

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Area of Science:

  • Neuroscience
  • Materials Science
  • Computational Biology

Background:

  • Metallic micro- and nanoparticles accumulate in the human hippocampus, correlating with neurotoxicity and neurodegenerative diseases.
  • Accurate segmentation and characterization of these particles are essential for understanding their neurobiological impact.

Purpose of the Study:

  • To present a novel computational method for precise segmentation and morphometric analysis of metallic nanoparticles in electron microscopy data.
  • To characterize the elemental composition of these particles using Energy-Dispersive X-ray Spectroscopy (EDS).

Main Methods:

  • A novel segmentation approach combining graph-cut theory and Dinic's algorithm for maximum flow computation.
  • Image data modeled as directed weighted graphs, with edge capacities derived from pixel intensities and gradients.
  • Validation against ground-truth masks yielding high Dice coefficients (0.97898 ± 0.0172) and Intersection over Union (IoU) (0.9609 ± 0.0326).

Main Results:

  • Robust segmentation of metallic nanoparticles in electron microscopy images.
  • Automatic extraction of morphometric parameters including area, perimeter, circularity, and Feret diameters.
  • Elemental analysis revealed heterogeneous compositions, with notable iron-rich particles and compounds containing nickel and chromium.

Conclusions:

  • The developed method enables accurate segmentation and characterization of metallic nanoparticles in neural tissue.
  • Observed compositional variability underscores the necessity of single-particle analysis for understanding neurobiological effects.
  • This approach contributes to elucidating the role of metallic deposits in neurodegeneration.