Fully automated registration of vibrational microspectroscopic images in histologically stained tissue sections. Issue 1 (December 2015)

• Record Type: Journal Article
• Authors: Yang, Chen; Niedieker, Daniel; Großerüschkamp, Frederik; Horn, Melanie; Tannapfel, Andrea; Kallenbach-Thieltges, Angela; Gerwert, Klaus; Mosig, Axel
• Is Part Of: BMC bioinformatics. Volume 16:Issue 1(2015)
• Page Start: 1
• Page End: 14
• Publication Date: 2015-12
• Subjects: Multimodal image registration -- fourier transform infrared microscopy -- Spectral histopathology; Bioinformatics -- Periodicals; Computational biology -- Periodicals; 570.285
• DOI: 10.1186/s12859-015-0804-9
• Languages: English
• ISSNs: 1471-2105

Abstract

Abstract Background In 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. Results We 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. Conclusions Our 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.