Computational cell quantification in the human brain tissues based on hard X-ray phase-contrast tomograms

Hieber, Simone E., Bikis, Christos, Khimchenko, Anna, Schulz, Georg, Deyhle, Hans, Thalmann, Peter, Chicherova, Natalia, Rack, Alexander, Zdora, Marie Christine, Zanette, Irene, Schweighauser, Gabriel, Hench, Jürgen and Müller, Bert (2016) Computational cell quantification in the human brain tissues based on hard X-ray phase-contrast tomograms. Wang, Ge, Stock, Stuart R. and Muller, Bert (eds.) In Developments in X-Ray Tomography X. vol. 9967, SPIE.. (doi:10.1117/12.2237012).

Abstract

Cell visualization and counting plays a crucial role in biological and medical research including the study of neurodegenerative diseases. The neuronal cell loss is typically determined to measure the extent of the disease. Its characterization is challenging because the cell density and size already differs by more than three orders of magnitude in a healthy cerebellum. Cell visualization is commonly performed by histology and fluorescence microscopy. These techniques are limited to resolve complex microstructures in the third dimension. Phase-contrast tomography has been proven to provide sufficient contrast in the three-dimensional imaging of soft tissue down to the cell level and, therefore, offers the basis for the three-dimensional segmentation. Within this context, a human cerebellum sample was embedded in paraffin and measured in local phase-contrast mode at the beamline ID19 (ESRF, Grenoble, France) and the Diamond Manchester Imaging Branchline I13-2 (Diamond Light Source, Didcot, UK). After the application of Frangi-based filtering the data showed sufficient contrast to automatically identify the Purkinje cells and to quantify their density to 177 cells per mm³ within the volume of interest. Moreover, brain layers were segmented in a region of interest based on edge detection. Subsequently performed histological analysis validated the presence of the cells, which required a mapping from the two-dimensional histological slices to the three-dimensional tomogram. The methodology can also be applied to further tissue types and shows potential for the computational tissue analysis in health and disease.

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