ArticleBMC bioinformatics2015
Fully automated registration of vibrational microspectroscopic images in histologically stained tissue sections.
Article in BMC bioinformatics, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- Cohort-Scale Spatial Autocorrelation for Tumor Prediction in Mid-Infrared Pathology and Spatial Biomarker Discovery Using MALDI Imaging Lipidomics.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- Statistical Considerations and Tools to Improve Histopathologic Protocols with Spectroscopic Imaging.Applied spectroscopy · 2022Article
- Article
- An Innovative Platform Merging Elemental Analysis and Ftir Imaging for Breast Tissue Analysis.Scientific reports · 2019Article
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Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundIn recent years, hyperspectral microscopy techniques such as infrared or Raman microscopy have been applied successfully for diagnostic purposes. In many of the corresponding studies, it is common practice to measure one and the same sample under different types of microscopes. Any joint analysis of the two image modalities requires to overlay the images, so that identical positions in the sample are located at the same coordinate in both images. This step, commonly referred to as image registration, has typically been performed manually in the lack of established automated computational registration tools.
resultsWe propose a corresponding registration algorithm that addresses this registration problem, and demonstrate the robustness of our approach in different constellations of microscopes. First, we deal with subregion registration of Fourier Transform Infrared (FTIR) microscopic images in whole-slide histopathological staining images. Second, we register FTIR imaged cores of tissue microarrays in their histopathologically stained counterparts, and finally perform registration of Coherent anti-Stokes Raman spectroscopic (CARS) images within histopathological staining images.
conclusionsOur validation involves a large variety of samples obtained from colon, bladder, and lung tissue on three different types of microscopes, and demonstrates that our proposed method works fully automated and highly robust in different constellations of microscopes involving diverse types of tissue samples.
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