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Fibered fluorescence microscopy of intra epidermal nerve fibers as translational marker for peripheral neuropathies in preclinical research – Processing and analysis of the data Host Publication: Knowledge for Growth 2008 Authors: F. Cornelissen, S. De Backer, J. Lemeire, R. Nuydens, T. Meert and P. Schelkens Publication Year: 2008
Abstract: Peripheral neuropathy is the consequence of damage to nerves of the peripheral nervous system and may be caused either by diseases, such as diabetes or AIDS but also develops as a dose limiting side-effect of chemotherapy. The damage to the sensory fibres results in changes in sensation, burning sensations, nerve pain, tingling or numbness, or an inability to determine joint position, which causes in-coordination. For many neuropathies, sensation changes often begin in the feet and progress toward the centre of the body with involvement of other areas as the condition worsens.
In a clinical setting disease progression is, besides the physical examination, also monitored by the immunohistochemical quantification of the intra epidermal nerve fibre density in skin biopsies taken in the extremities. This approach however is very labour intensive and sensitive to variations. In preclinical research the combination of Thyǃ/YFP mice with FFM allows for the, non-invasive, longitudinal in vivo assessment of the IENFD in various models for peripheral neuropathies. The FFM allows the real time visualization of a larger surface (or field of View - FOV) as compared to skin punch biopsies which are typical only 2Dž mm diameter. The acquired image sequence can then be used to extract quantitative parameters such as nerve density, calibre and arborisation.
To construct this large FOV, denoising (increasing signal to noise ratio), deconvolution (enhancing details), and construction of mosaic images with sub-pixel alignment, is indispensable. All transformation information must be integrated into a global consistent image aligning. For longitudinal analysis, mosaics captured at different times must be aligned as well. Since images are generated a large pace, use of algorithms working on multiple CPU's (parallel processor/cluster/grid) is imperative. We are also investigating the possibility of speeding up the process by using General-purpose Graphics Processor Units (GPGPU's). For Alignment, Affine transformations are compared with scale-space invariant interesting points matching.
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