The variation in specific energy absorbed to different cell compartments caused by variations in size and chemical composition is poorly investigated in radiotherapy. The aim of this study was to develop an algorithm to derive cell and cell nuclei size distributions from 2D histology samples, and build 3D cellular geometries to provide Monte Carlo (MC)-based dose calculation engines with a morphologically relevant input geometry. Stained and unstained regions of the histology samples are segmented using a Gaussian mixture model, and individual cell nuclei are identified via thresholding. Delaunay triangulation is applied to determine the distribution of distances between the centroids of nearest neighbour cells. A pouring simulation is used to build a 3D virtual tissue sample, with cell radii randomised according to the cell size distribution determined from the histology samples. A slice with the same thickness as the histology sample is cut through the 3D data and characterised in the same way as the measured histology. The comparison between this virtual slice and the measured histology is used to adjust the initial cell size distribution into the pouring simulation. This iterative approach of a pouring simulation with adjustments guided by comparison is continued until an input cell size distribution is found that yields a distribution in the sliced geometry that agrees with the measured histology samples. The thus obtained morphologically realistic 3D cellular geometry can be used as input to MC-based dose calculation programs for studies of dose response due to variations in morphology and size of tumour/healthy tissue cells/nuclei, and extracellular material.
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