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Alexandrov, T.* ; Meding, S. ; Trede, D.* ; Kobarg, J.H.* ; Balluff, B. ; Walch, A.K. ; Thiele, H.* ; Maass, P.*

Super-resolution segmentation of imaging mass spectrometry data: Solving the issue of low lateral resolution.

J. Proteomics 75, 237-245 (2011)
DOI PMC
Open Access Green as soon as Postprint is submitted to ZB.
In the last decade, imaging mass spectrometry has seen incredible technological advances in its applications to biological samples. One computational method of data mining in this field is the spatial segmentation of a sample, which produces a segmentation map highlighting chemically similar regions. An important issue for any imaging mass spectrometry technology is its relatively low spatial or lateral resolution (i.e. a large size of pixel) as compared with microscopy. Thus, the spatial resolution of a segmentation map is also relatively low, that complicates its visual examination and interpretation when compared with microscopy data, as well as reduces the accuracy of any automated comparison. We address this issue by proposing an approach to improve the spatial resolution of a segmentation map. Given a segmentation map, our method magnifies it up to some factor, producing a super-resolution segmentation map. The super-resolution map can be overlaid and compared with a high-res microscopy image. The proposed method is based on recent advances in image processing and smoothes the "pixilated" region boundaries while preserving fine details. Moreover, it neither eliminates nor splits any region. We evaluated the proposed super-resolution segmentation approach on three MALDI-imaging datasets of human tissue sections and demonstrated the superiority of the super-segmentation maps over standard segmentation maps.
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Publication type Article: Journal article
Document type Scientific Article
Keywords Imaging mass spectrometry; Segmentation map; Spatial resolution; Computational super-resolution
ISSN (print) / ISBN 1874-3919
e-ISSN 1876-7737
Journal Journal of Proteomics
Quellenangaben Volume: 75, Issue: 1, Pages: 237-245 Article Number: , Supplement: ,
Publisher Elsevier
Reviewing status Peer reviewed
Institute(s) Institute of Pathology (PATH)
Translational Metabolic Oncology (IDC-TMO)
Research Unit Analytical Pathology (AAP)